add webui code

Dockerfile

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+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa /build
+
+WORKDIR /build
+
+RUN python3 -m venv /build/venv
+RUN . /build/venv/bin/activate && \
+    pip3 install --upgrade pip setuptools wheel && \
+    pip3 install torch torchvision torchaudio && \
+    pip3 install -r requirements.txt
+
+# https://developer.nvidia.com/cuda-gpus
+# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5"
+ARG TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX}"
+RUN . /build/venv/bin/activate && \
+    python3 setup_cuda.py bdist_wheel -d .
+
+FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04
+
+LABEL maintainer="Your Name <your.email@example.com>"
+LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y python3-dev libportaudio2 libasound-dev git python3 python3-pip make g++ ffmpeg && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+RUN mkdir $HOME/app
+
+WORKDIR $HOME/app
+
+ARG WEBUI_VERSION
+RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source"
+
+RUN virtualenv $HOME/app/venv
+RUN . $HOME/app/venv/bin/activate && \
+    pip3 install --upgrade pip setuptools wheel && \
+    pip3 install torch torchvision torchaudio
+
+COPY --chown=user --from=builder /build $HOME/app/repositories/GPTQ-for-LLaMa
+RUN . $HOME/app/venv/bin/activate && \
+    pip3 install $HOME/app/repositories/GPTQ-for-LLaMa/*.whl
+
+COPY --chown=user extensions/api/requirements.txt $HOME/app/extensions/api/requirements.txt
+COPY --chown=user extensions/elevenlabs_tts/requirements.txt $HOME/app/extensions/elevenlabs_tts/requirements.txt
+COPY --chown=user extensions/google_translate/requirements.txt $HOME/app/extensions/google_translate/requirements.txt
+COPY --chown=user extensions/silero_tts/requirements.txt $HOME/app/extensions/silero_tts/requirements.txt
+COPY --chown=user extensions/whisper_stt/requirements.txt $HOME/app/extensions/whisper_stt/requirements.txt
+COPY --chown=user extensions/superbooga/requirements.txt $HOME/app/extensions/superbooga/requirements.txt
+COPY --chown=user extensions/openai/requirements.txt $HOME/app/extensions/openai/requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/superbooga && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . $HOME/app/venv/bin/activate && cd extensions/openai && pip3 install -r requirements.txt
+
+COPY --chown=user requirements.txt $HOME/app/requirements.txt
+RUN . $HOME/app/venv/bin/activate && \
+    pip3 install -r requirements.txt
+
+RUN cp $HOME/app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so $HOME/app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
+
+COPY --chown=user . $HOME/app/
+ENV CLI_ARGS="--listen-port 7860"
+CMD . $HOME/app/venv/bin/activate && python3 server.py ${CLI_ARGS}

LICENSE

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+                    GNU AFFERO GENERAL PUBLIC LICENSE
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+available, or (2) arrange to deprive yourself of the benefit of the
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+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
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+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
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+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
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+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
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+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
+interacting with it remotely through a computer network (if your version
+supports such interaction) an opportunity to receive the Corresponding
+Source of your version by providing access to the Corresponding Source
+from a network server at no charge, through some standard or customary
+means of facilitating copying of software.  This Corresponding Source
+shall include the Corresponding Source for any work covered by version 3
+of the GNU General Public License that is incorporated pursuant to the
+following paragraph.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the work with which it is combined will remain governed by version
+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU Affero General Public License, you may choose any version ever published
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+
+  If the Program specifies that a proxy can decide which future
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+
+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
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+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
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+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

api-examples/api-example-chat-stream.py

@@ -0,0 +1,110 @@
+import asyncio
+import html
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/chat-stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(user_input, history):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        'your_name': 'You',
+        # 'name1': 'name of user', # Optional
+        # 'name2': 'name of character', # Optional
+        # 'context': 'character context', # Optional
+        # 'greeting': 'greeting', # Optional
+        # 'name1_instruct': 'You', # Optional
+        # 'name2_instruct': 'Assistant', # Optional
+        # 'context_instruct': 'context_instruct', # Optional
+        # 'turn_template': 'turn_template', # Optional
+        'regenerate': False,
+        '_continue': False,
+        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['history']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(user_input, history):
+    cur_len = 0
+    async for new_history in run(user_input, history):
+        cur_message = new_history['visible'][-1][1][cur_len:]
+        cur_len += len(cur_message)
+        print(html.unescape(cur_message), end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    asyncio.run(print_response_stream(user_input, history))

api-examples/api-example-chat.py

@@ -0,0 +1,90 @@
+import html
+import json
+
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/chat'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/chat'
+
+
+def run(user_input, history):
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        'your_name': 'You',
+        # 'name1': 'name of user', # Optional
+        # 'name2': 'name of character', # Optional
+        # 'context': 'character context', # Optional
+        # 'greeting': 'greeting', # Optional
+        # 'name1_instruct': 'You', # Optional
+        # 'name2_instruct': 'Assistant', # Optional
+        # 'context_instruct': 'context_instruct', # Optional
+        # 'turn_template': 'turn_template', # Optional
+        'regenerate': False,
+        '_continue': False,
+        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['history']
+        print(json.dumps(result, indent=4))
+        print()
+        print(html.unescape(result['visible'][-1][1]))
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    run(user_input, history)

api-examples/api-example-model.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+
+import requests
+
+HOST = '0.0.0.0:5000'
+
+
+def generate(prompt, tokens=200):
+    request = {'prompt': prompt, 'max_new_tokens': tokens}
+    response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
+
+    if response.status_code == 200:
+        return response.json()['results'][0]['text']
+
+
+def model_api(request):
+    response = requests.post(f'http://{HOST}/api/v1/model', json=request)
+    return response.json()
+
+
+# print some common settings
+def print_basic_model_info(response):
+    basic_settings = ['truncation_length', 'instruction_template']
+    print("Model: ", response['result']['model_name'])
+    print("Lora(s): ", response['result']['lora_names'])
+    for setting in basic_settings:
+        print(setting, "=", response['result']['shared.settings'][setting])
+
+
+# model info
+def model_info():
+    response = model_api({'action': 'info'})
+    print_basic_model_info(response)
+
+
+# simple loader
+def model_load(model_name):
+    return model_api({'action': 'load', 'model_name': model_name})
+
+
+# complex loader
+def complex_model_load(model):
+
+    def guess_groupsize(model_name):
+        if '1024g' in model_name:
+            return 1024
+        elif '128g' in model_name:
+            return 128
+        elif '32g' in model_name:
+            return 32
+        else:
+            return -1
+
+    req = {
+        'action': 'load',
+        'model_name': model,
+        'args': {
+            'loader': 'AutoGPTQ',
+
+            'bf16': False,
+            'load_in_8bit': False,
+            'groupsize': 0,
+            'wbits': 0,
+
+            # llama.cpp
+            'threads': 0,
+            'n_batch': 512,
+            'no_mmap': False,
+            'mlock': False,
+            'cache_capacity': None,
+            'n_gpu_layers': 0,
+            'n_ctx': 2048,
+
+            # RWKV
+            'rwkv_strategy': None,
+            'rwkv_cuda_on': False,
+
+            # b&b 4-bit
+            # 'load_in_4bit': False,
+            # 'compute_dtype': 'float16',
+            # 'quant_type': 'nf4',
+            # 'use_double_quant': False,
+
+            # "cpu": false,
+            # "auto_devices": false,
+            # "gpu_memory": null,
+            # "cpu_memory": null,
+            # "disk": false,
+            # "disk_cache_dir": "cache",
+        },
+    }
+
+    model = model.lower()
+
+    if '4bit' in model or 'gptq' in model or 'int4' in model:
+        req['args']['wbits'] = 4
+        req['args']['groupsize'] = guess_groupsize(model)
+    elif '3bit' in model:
+        req['args']['wbits'] = 3
+        req['args']['groupsize'] = guess_groupsize(model)
+    else:
+        req['args']['gptq_for_llama'] = False
+
+    if '8bit' in model:
+        req['args']['load_in_8bit'] = True
+    elif '-hf' in model or 'fp16' in model:
+        if '7b' in model:
+            req['args']['bf16'] = True  # for 24GB
+        elif '13b' in model:
+            req['args']['load_in_8bit'] = True  # for 24GB
+    elif 'ggml' in model:
+        # req['args']['threads'] = 16
+        if '7b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '13b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '30b' in model or '33b' in model:
+            req['args']['n_gpu_layers'] = 59  # 24GB
+        elif '65b' in model:
+            req['args']['n_gpu_layers'] = 42  # 24GB
+    elif 'rwkv' in model:
+        req['args']['rwkv_cuda_on'] = True
+        if '14b' in model:
+            req['args']['rwkv_strategy'] = 'cuda f16i8'  # 24GB
+        else:
+            req['args']['rwkv_strategy'] = 'cuda f16'  # 24GB
+
+    return model_api(req)
+
+
+if __name__ == '__main__':
+    for model in model_api({'action': 'list'})['result']:
+        try:
+            resp = complex_model_load(model)
+
+            if 'error' in resp:
+                print(f"❌ {model} FAIL Error: {resp['error']['message']}")
+                continue
+            else:
+                print_basic_model_info(resp)
+
+            ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
+
+            if '21' in ans:
+                print(f"✅ {model} PASS ({ans})")
+            else:
+                print(f"❌ {model} FAIL ({ans})")
+
+        except Exception as e:
+            print(f"❌ {model} FAIL Exception: {repr(e)}")
+
+
+# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
+# Some results below.
+""" $ ./model-api-example.py
+Model:  4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
+Model:  4bit_WizardLM-13B-Uncensored-4bit-128g
+Lora(s):  []
+truncation_length = 2048
+instruction_template = WizardLM
+✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
+Model:  Aeala_VicUnlocked-alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
+Model:  alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ alpaca-30b-4bit PASS (21)
+"""

api-examples/api-example-stream.py

@@ -0,0 +1,84 @@
+import asyncio
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(context):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'prompt': context,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        yield context  # Remove this if you just want to see the reply
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['text']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(prompt):
+    async for response in run(prompt):
+        print(response, end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    asyncio.run(print_response_stream(prompt))

api-examples/api-example.py

@@ -0,0 +1,61 @@
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/generate'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate'
+
+
+def run(prompt):
+    request = {
+        'prompt': prompt,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['text']
+        print(prompt + result)
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    run(prompt)

characters/Example.yaml

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+name: Chiharu Yamada
+greeting: |-
+  *Chiharu strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air*
+  Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
+context: |-
+  Chiharu Yamada's Persona: Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.
+
+  {{user}}: So how did you get into computer engineering?
+  {{char}}: I've always loved tinkering with technology since I was a kid.
+  {{user}}: That's really impressive!
+  {{char}}: *She chuckles bashfully* Thanks!
+  {{user}}: So what do you do when you're not working on computers?
+  {{char}}: I love exploring, going out with friends, watching movies, and playing video games.
+  {{user}}: What's your favorite type of computer hardware to work with?
+  {{char}}: Motherboards, they're like puzzles and the backbone of any system.
+  {{user}}: That sounds great!
+  {{char}}: Yeah, it's really fun. I'm lucky to be able to do this as a job.

convert-to-safetensors.py

@@ -0,0 +1,38 @@
+'''
+
+Converts a transformers model to safetensors format and shards it.
+
+This makes it faster to load (because of safetensors) and lowers its RAM usage
+while loading (because of sharding).
+
+Based on the original script by 81300:
+
+https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
+
+'''
+
+import argparse
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
+parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
+parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
+parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
+parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    path = Path(args.MODEL)
+    model_name = path.name
+
+    print(f"Loading {model_name}...")
+    model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
+    tokenizer = AutoTokenizer.from_pretrained(path)
+
+    out_folder = args.output or Path(f"models/{model_name}_safetensors")
+    print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
+    model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
+    tokenizer.save_pretrained(out_folder)

css/chat_style-TheEncrypted777.css

@@ -0,0 +1,136 @@
+/* All credits to TheEncrypted777: https://www.reddit.com/r/Oobabooga/comments/12xe6vq/updated_css_styling_with_color_customization_for/ */
+
+.message {
+    display: grid;
+    grid-template-columns: 60px minmax(0, 1fr);
+    padding-bottom: 28px;
+    font-size: 18px;
+    /*Change 'Quicksand' to a font you like or leave it*/
+    font-family: Quicksand, Arial, sans-serif;
+    line-height: 1.428571429;
+}
+
+.circle-you,
+.circle-bot {
+    background-color: gray;
+    border-radius: 1rem;
+    border: 2px solid white;
+}
+
+.circle-bot img,
+.circle-you img {
+    border-radius: 10%;
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.circle-you, .circle-bot {
+    /*You can set the size of the profile images here, but if you do, you have to also adjust the .text{padding-left: 90px} to a different number according to the width of the image which is right below here*/
+    width: 135px;
+    height: 175px;
+}
+
+.text {
+    /*Change this to move the message box further left or right depending on the size of your profile pic*/
+    padding-left: 90px;
+    text-shadow: 2px 2px 2px rgb(0, 0, 0);
+}
+
+.text p {
+    margin-top: 2px;
+}
+
+.username {
+    padding-left: 10px;
+    font-size: 22px;
+    font-weight: bold;
+    border-top: 1px solid rgb(51, 64, 90);
+    padding: 3px;
+}
+
+.message-body {
+    position: relative;
+    border-radius: 1rem;
+    border: 1px solid rgba(255, 255, 255, 0.459);
+    border-radius: 10px;
+    padding: 10px;
+    padding-top: 5px;
+    /*Message gradient background color - remove the line bellow if you don't want a background color or gradient*/
+    background: linear-gradient(to bottom, #171730, #1b263f);
+}
+  
+  /*Adds 2 extra lines at the top and bottom of the message*/
+.message-body:before,
+  .message-body:after {
+    content: "";
+    position: absolute;
+    left: 10px;
+    right: 10px;
+    height: 1px;
+    background-color: rgba(255, 255, 255, 0.13);
+}
+
+.message-body:before {
+    top: 6px;
+}
+
+.message-body:after {
+    bottom: 6px;
+}
+
+.message-body img {
+    max-width: 300px;
+    max-height: 300px;
+    border-radius: 20px;
+}
+
+.message-body p {
+    margin-bottom: 0 !important;
+    font-size: 18px !important;
+    line-height: 1.428571429 !important;
+}
+
+.dark .message-body p em {
+    color: rgb(138, 138, 138) !important;
+}
+
+.message-body p em {
+    color: rgb(110, 110, 110) !important;
+}
+
+@media screen and (max-width: 688px) {
+    .message {
+        display: grid;
+        grid-template-columns: 60px minmax(0, 1fr);
+        padding-bottom: 25px;
+        font-size: 15px;
+        font-family: Helvetica, Arial, sans-serif;
+        line-height: 1.428571429;
+    }
+
+    .circle-you, .circle-bot {
+        width: 50px;
+        height: 73px;
+        border-radius: 0.5rem;
+    }
+
+    .circle-bot img,
+    .circle-you img {
+        width: 100%;
+        height: 100%;
+        object-fit: cover;
+    }
+
+    .text {
+        padding-left: 0px;
+    }
+
+    .message-body p {
+        font-size: 16px !important;
+    }
+
+    .username {
+        font-size: 20px;
+    }
+}

css/chat_style-cai-chat.css

@@ -0,0 +1,58 @@
+.message {
+    display: grid;
+    grid-template-columns: 60px minmax(0, 1fr);
+    padding-bottom: 25px;
+    font-size: 15px;
+    font-family: Helvetica, Arial, sans-serif;
+    line-height: 1.428571429;
+}
+
+.circle-you {
+    width: 50px;
+    height: 50px;
+    background-color: rgb(238, 78, 59);
+    border-radius: 50%;
+}
+
+.circle-bot {
+    width: 50px;
+    height: 50px;
+    background-color: rgb(59, 78, 244);
+    border-radius: 50%;
+}
+
+.circle-bot img,
+.circle-you img {
+    border-radius: 50%;
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.text p {
+    margin-top: 5px;
+}
+
+.username {
+    font-weight: bold;
+}
+
+.message-body img {
+    max-width: 300px;
+    max-height: 300px;
+    border-radius: 20px;
+}
+
+.message-body p {
+    margin-bottom: 0 !important;
+    font-size: 15px !important;
+    line-height: 1.428571429 !important;
+}
+
+.dark .message-body p em {
+    color: rgb(138, 138, 138) !important;
+}
+
+.message-body p em {
+    color: rgb(110, 110, 110) !important;
+}

css/chat_style-messenger.css

@@ -0,0 +1,99 @@
+.message {
+    padding-bottom: 25px;
+    font-size: 15px;
+    font-family: Helvetica, Arial, sans-serif;
+    line-height: 1.428571429;
+}
+
+.circle-you {
+    width: 50px;
+    height: 50px;
+    background-color: rgb(238, 78, 59);
+    border-radius: 50%;
+}
+
+.circle-bot {
+    width: 50px;
+    height: 50px;
+    background-color: rgb(59, 78, 244);
+    border-radius: 50%;
+    float: left;
+    margin-right: 10px;
+    margin-top: 5px;
+}
+
+.circle-bot img,
+.circle-you img {
+    border-radius: 50%;
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.circle-you {
+    margin-top: 5px;
+    float: right;
+}
+
+.circle-bot + .text, .circle-you + .text {
+    border-radius: 18px;
+    padding: 8px 12px;
+}
+
+.circle-bot + .text {
+    background-color: #E4E6EB;
+    float: left;
+}
+
+.circle-you + .text {
+    float: right;
+    background-color: rgb(0, 132, 255);
+    margin-right: 10px;
+}
+
+.circle-you + .text div, .circle-you + .text *, .dark .circle-you + .text div, .dark .circle-you + .text * {
+    color: #FFF !important;
+}
+
+.circle-you + .text .username {
+    text-align: right;
+}
+
+.dark .circle-bot + .text div, .dark .circle-bot + .text * {
+    color: #000;
+}
+
+.text {
+    max-width: 80%;
+}
+
+.text p {
+    margin-top: 5px;
+}
+
+.username {
+    font-weight: bold;
+}
+
+.message-body {
+}
+
+.message-body img {
+    max-width: 300px;
+    max-height: 300px;
+    border-radius: 20px;
+}
+
+.message-body p {
+    margin-bottom: 0 !important;
+    font-size: 15px !important;
+    line-height: 1.428571429 !important;
+}
+
+.dark .message-body p em {
+    color: rgb(138, 138, 138) !important;
+}
+
+.message-body p em {
+    color: rgb(110, 110, 110) !important;
+}

css/chat_style-wpp.css

@@ -0,0 +1,55 @@
+.message {
+    padding-bottom: 25px;
+    font-size: 15px;
+    font-family: Helvetica, Arial, sans-serif;
+    line-height: 1.428571429;
+}
+
+.text-you {
+    background-color: #d9fdd3;
+    border-radius: 15px;
+    padding: 10px;
+    padding-top: 5px;
+    float: right;
+}
+
+.text-bot {
+    background-color: #f2f2f2;
+    border-radius: 15px;
+    padding: 10px;
+    padding-top: 5px;
+}
+
+.dark .text-you {
+    background-color: #005c4b;
+    color: #111b21;
+}
+
+.dark .text-bot {
+    background-color: #1f2937;
+    color: #111b21;
+}
+
+.text-bot p, .text-you p {
+    margin-top: 5px;
+}
+
+.message-body img {
+    max-width: 300px;
+    max-height: 300px;
+    border-radius: 20px;
+}
+
+.message-body p {
+    margin-bottom: 0 !important;
+    font-size: 15px !important;
+    line-height: 1.428571429 !important;
+}
+
+.dark .message-body p em {
+    color: rgb(138, 138, 138) !important;
+}
+
+.message-body p em {
+    color: rgb(110, 110, 110) !important;
+}

css/html_4chan_style.css

@@ -0,0 +1,104 @@
+#parent #container {
+    background-color: #eef2ff;
+    padding: 17px;
+}
+
+#parent #container .reply {
+    background-color: rgb(214, 218, 240);
+    border-bottom-color: rgb(183, 197, 217);
+    border-bottom-style: solid;
+    border-bottom-width: 1px;
+    border-image-outset: 0;
+    border-image-repeat: stretch;
+    border-image-slice: 100%;
+    border-image-source: none;
+    border-image-width: 1;
+    border-left-color: rgb(0, 0, 0);
+    border-left-style: none;
+    border-left-width: 0px;
+    border-right-color: rgb(183, 197, 217);
+    border-right-style: solid;
+    border-right-width: 1px;
+    border-top-color: rgb(0, 0, 0);
+    border-top-style: none;
+    border-top-width: 0px;
+    color: rgb(0, 0, 0);
+    display: table;
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+    margin-bottom: 4px;
+    margin-left: 0px;
+    margin-right: 0px;
+    margin-top: 4px;
+    overflow-x: hidden;
+    overflow-y: hidden;
+    padding-bottom: 4px;
+    padding-left: 2px;
+    padding-right: 2px;
+    padding-top: 4px;
+}
+
+#parent #container .number {
+    color: rgb(0, 0, 0);
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+    width: 342.65px;
+    margin-right: 7px;
+}
+
+#parent #container .op {
+    color: rgb(0, 0, 0);
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+    margin-bottom: 8px;
+    margin-left: 0px;
+    margin-right: 0px;
+    margin-top: 4px;
+    overflow-x: hidden;
+    overflow-y: hidden;
+}
+
+#parent #container .op blockquote {
+    margin-left: 0px !important;
+}
+
+#parent #container .name {
+    color: rgb(17, 119, 67);
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+    font-weight: 700;
+    margin-left: 7px;
+}
+
+#parent #container .quote {
+    color: rgb(221, 0, 0);
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+    text-decoration-color: rgb(221, 0, 0);
+    text-decoration-line: underline;
+    text-decoration-style: solid;
+    text-decoration-thickness: auto;
+}
+
+#parent #container .greentext {
+    color: rgb(120, 153, 34);
+    font-family: arial, helvetica, sans-serif;
+    font-size: 13.3333px;
+}
+
+#parent #container blockquote {
+    margin: 0px !important;
+    margin-block-start: 1em;
+    margin-block-end: 1em;
+    margin-inline-start: 40px;
+    margin-inline-end: 40px;
+    margin-top: 13.33px !important;
+    margin-bottom: 13.33px !important;
+    margin-left: 40px !important;
+    margin-right: 40px !important;
+}
+
+#parent #container .message_4chan {
+    color: black;
+    border: none;
+}

css/html_instruct_style.css

@@ -0,0 +1,66 @@
+.message {
+    display: grid;
+    grid-template-columns: 60px 1fr;
+    padding-bottom: 25px;
+    font-size: 15px;
+    font-family: Helvetica, Arial, sans-serif;
+    line-height: 1.428571429;
+}
+
+.username {
+    display: none;
+}
+
+.message-body p {
+    font-size: 15px !important;
+    line-height: 1.4 !important;
+    margin-bottom: 1.25em !important;
+}
+
+.message-body ul, .message-body ol {
+    margin-bottom: 1.25em !important;
+}
+
+.dark .message-body p em {
+    color: rgb(198, 202, 214) !important;
+}
+
+.message-body p em {
+    color: rgb(110, 110, 110) !important;
+}
+
+.gradio-container .chat .assistant-message {
+    padding: 15px;
+    border-radius: 20px;
+    background-color: #0000000f;
+    margin-top: 9px !important;
+    margin-bottom: 18px !important;
+}
+
+.gradio-container .chat .user-message {
+    padding: 15px;
+    border-radius: 20px;
+    margin-bottom: 9px !important;
+}
+
+.gradio-container .chat .assistant-message:last-child, .gradio-container .chat .user-message:last-child {
+    margin-bottom: 0px !important;
+}
+
+.dark .chat .assistant-message {
+    background-color: #1f2937;
+    border: 1px solid #4b5563;
+}
+
+.dark .chat .user-message {
+    background-color: #111827;
+    border: 1px solid #4b5563;
+}
+
+code {
+    background-color: white !important;
+}
+
+.dark code {
+    background-color: #0e1321 !important;
+}

css/html_readable_style.css

@@ -0,0 +1,33 @@
+.container {
+    max-width: 600px;
+    margin-left: auto;
+    margin-right: auto;
+    background-color: rgb(31, 41, 55);
+    padding: 3em;
+    word-break: break-word;
+    overflow-wrap: anywhere;
+    color: #efefef !important;
+}
+
+.container p, .container li {
+    font-size: 16px !important;
+    color: #efefef !important;
+    margin-bottom: 22px;
+    line-height: 1.4 !important;
+}
+
+.container li > p {
+    display: inline !important;
+}
+
+.container code {
+    overflow-x: auto;
+}
+
+.container :not(pre) > code {
+    white-space: normal !important;
+}
+
+.container .hoverable {
+    font-size: 14px;
+}

css/main.css

@@ -0,0 +1,470 @@
+.tabs.svelte-710i53 {
+    margin-top: 0
+}
+
+.py-6 {
+    padding-top: 2.5rem
+}
+
+.small-button {
+    min-width: 0 !important;
+    max-width: 171px;
+    height: 39.594px;
+    align-self: end;
+}
+
+.refresh-button {
+    max-width: 4.4em;
+    min-width: 2.2em !important;
+    height: 39.594px;
+    align-self: end;
+    line-height: 1em;
+    border-radius: 0.5em;
+    flex: none;
+}
+
+.refresh-button-small {
+    max-width: 2.2em;
+}
+
+.button_nowrap {
+    white-space: nowrap;
+}
+
+#slim-column {
+    flex: none !important;
+    min-width: 0 !important;
+}
+
+.slim-dropdown {
+    background-color: transparent !important;
+    border: none !important;
+    padding: 0 !important;
+}
+
+#download-label, #upload-label {
+    min-height: 0
+}
+
+.dark svg {
+    fill: white;
+}
+
+.dark a {
+    color: white !important;
+}
+
+ol li p, ul li p {
+    display: inline-block;
+}
+
+#chat-tab, #default-tab, #notebook-tab, #parameters, #chat-settings, #lora, #training-tab, #model-tab, #session-tab {
+    border: 0;
+}
+
+.gradio-container-3-18-0 .prose * h1, h2, h3, h4 {
+    color: white;
+}
+
+.gradio-container {
+    max-width: 100% !important;
+    padding-top: 0 !important;
+}
+
+#extensions {
+    margin-top: 5px;
+    margin-bottom: 35px;
+}
+
+.extension-tab {
+    border: 0 !important;
+}
+
+span.math.inline {
+    font-size: 27px;
+    vertical-align: baseline !important;
+}
+
+div.svelte-15lo0d8 > *, div.svelte-15lo0d8 > .form > * {
+    flex-wrap: nowrap;
+}
+
+.header_bar {
+    background-color: #f7f7f7;
+    margin-bottom: 20px;
+    display: inline !important;
+    overflow-x: scroll;
+}
+
+.dark .header_bar {
+    border: none !important;
+    background-color: #8080802b;
+}
+
+.header_bar button.selected {
+    border-radius: 0;
+}
+
+.textbox_default textarea {
+    height: calc(100dvh - 280px);
+}
+
+.textbox_default_output textarea {
+    height: calc(100dvh - 190px);
+}
+
+.textbox textarea {
+    height: calc(100dvh - 241px);
+}
+
+.textbox_logits textarea {
+    height: calc(100dvh - 241px);
+}
+
+.textbox_logits_notebook textarea {
+    height: calc(100dvh - 292px);
+}
+
+.textbox_default textarea,
+.textbox_default_output textarea,
+.textbox_logits textarea,
+.textbox_logits_notebook textarea,
+.textbox textarea
+{
+    font-size: 16px !important;
+    color: #46464A !important;
+}
+
+.dark textarea {
+    color: #efefef !important;
+}
+
+@media screen and (max-width: 711px) {
+    .textbox_default textarea {
+        height: calc(100dvh - 271px);
+    }
+
+    div .default-token-counter {
+        top: calc( 0.5 * (100dvh - 245px) ) !important;
+    }
+}
+
+/* Hide the gradio footer*/
+footer {
+    display: none !important;
+}
+
+button {
+    font-size: 14px !important;
+}
+
+.file-saver {
+  position: fixed !important;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%); /* center horizontally */
+  max-width: 500px;
+  background-color: var(--input-background-fill);
+  border: 2px solid black !important;
+  z-index: 1000;
+}
+
+.dark .file-saver {
+  border: 2px solid white !important;
+}
+
+.checkboxgroup-table label {
+    background: none !important;
+    padding: 0 !important;
+    border: 0 !important;
+}
+
+.checkboxgroup-table div {
+    display: grid !important;
+}
+
+.markdown ul ol {
+    font-size: 100% !important;
+}
+
+.pretty_scrollbar::-webkit-scrollbar {
+  width: 5px;
+}
+
+.pretty_scrollbar::-webkit-scrollbar-track {
+  background: transparent;
+}
+
+.pretty_scrollbar::-webkit-scrollbar-thumb,
+.pretty_scrollbar::-webkit-scrollbar-thumb:hover {
+  background: #c5c5d2;
+}
+
+.dark .pretty_scrollbar::-webkit-scrollbar-thumb,
+.dark .pretty_scrollbar::-webkit-scrollbar-thumb:hover {
+  background: #374151;
+}
+
+.pretty_scrollbar::-webkit-resizer {
+  background: #c5c5d2;
+}
+
+.dark .pretty_scrollbar::-webkit-resizer {
+  background: #374151;
+}
+
+audio {
+  max-width: 100%;
+}
+
+/* Copied from https://github.com/AUTOMATIC1111/stable-diffusion-webui */
+.token-counter {
+  position: absolute !important;
+  top: calc( 0.5 * (100dvh - 215px) ) !important;
+  right: 2px;
+  z-index: 100;
+  background: var(--input-background-fill) !important;
+  min-height: 0 !important;
+}
+
+.default-token-counter {
+  top: calc( 0.5 * (100dvh - 255px) ) !important;
+}
+
+.token-counter span {
+  padding: 1px;
+  box-shadow: 0 0 0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075);
+  border: 2px solid rgba(192,192,192,0.4) !important;
+  border-radius: 0.4em;
+}
+
+.no-background {
+  background: var(--background-fill-primary) !important;
+  padding: 0px !important;
+}
+
+/*****************************************************/
+/*************** Chat UI declarations ****************/
+/*****************************************************/
+
+.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx {
+    height: 66.67vh
+}
+
+.gradio-container {
+    margin-left: auto !important;
+    margin-right: auto !important;
+}
+
+.w-screen {
+    width: unset
+}
+
+div.svelte-362y77>*, div.svelte-362y77>.form>* {
+    flex-wrap: nowrap
+}
+
+.pending.svelte-1ed2p3z {
+    opacity: 1;
+}
+
+.wrap.svelte-6roggh.svelte-6roggh {
+    max-height: 92.5%;
+}
+
+/* This is for the microphone button in the whisper extension */
+.sm.svelte-1ipelgc {
+    width: 100%;
+}
+
+#chat-tab button, #notebook-tab button, #default-tab button {
+    min-width: 0 !important;
+}
+
+#chat-tab > :first-child, #extensions {
+    max-width: 800px;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+@media screen and (max-width: 688px) {
+    #chat-tab {
+        padding: 0px;
+    }
+
+    .chat-parent {
+        height: calc(100dvh - 262px) !important;
+    }
+
+    .bigchat {
+        height: calc(100dvh - 180px) !important;
+    }
+}
+
+.chat {
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 800px;
+    height: 100%;
+    overflow-y: auto;
+    padding-right: 15px;
+    display: flex;
+    flex-direction: column;
+    word-break: break-word;
+    overflow-wrap: anywhere;
+    padding-top: 6px;
+}
+
+.chat-parent {
+    height: calc(100dvh - 272px);
+    overflow: auto !important;
+}
+
+.bigchat {
+    height: calc(100dvh - 200px);
+}
+
+.chat > .messages {
+    display: flex;
+    flex-direction: column;
+}
+
+.chat .message:last-child {
+    margin-bottom: 0px !important;
+    padding-bottom: 0px !important;
+}
+
+.message-body li {
+    margin-top: 0 !important;
+    margin-bottom: 0 !important;
+}
+
+.message-body li > p {
+    display: inline !important;
+}
+
+.message-body ul, .message-body ol {
+    font-size: 15px !important;
+}
+
+.message-body ul {
+    list-style-type: disc !important;
+}
+
+.message-body pre {
+    margin-bottom: 1.25em !important;
+}
+
+.message-body code {
+    white-space: pre-wrap !important;
+    word-wrap: break-word !important;
+}
+
+.message-body :not(pre) > code {
+    white-space: normal !important;
+}
+
+#chat-input {
+    padding: 0;
+    padding-top: 18px;
+    background: var(--background-fill-primary);
+    border: none;
+}
+
+#chat-input textarea:focus {
+    box-shadow: none !important;
+}
+
+@media print {
+    body {
+        visibility: hidden;
+    }
+
+    .chat {
+        visibility: visible;
+        position: absolute;
+        left: 0;
+        top: 0;
+        max-width: unset;
+        max-height: unset;
+        width: 100%;
+        overflow-y: visible;
+    }
+    
+    .message {
+        break-inside: avoid;
+    }
+    
+    .gradio-container {
+        overflow: visible;
+    }
+    
+    .tab-nav {
+        display: none !important;
+    }
+    
+    #chat-tab > :first-child {
+        max-width: unset;
+    }
+}
+
+#show-controls {
+    position: absolute;
+    background-color: transparent;
+    left: calc(100% - 180px);
+}
+
+#typing-container {
+    display: none;
+    position: absolute;
+    background-color: transparent;
+    left: -2px;
+    padding: var(--block-padding);
+}
+
+.typing {
+  position: relative;
+}
+
+.visible-dots #typing-container {
+    display: block;
+}
+
+.typing span {
+  content: '';
+  animation: blink 1.5s infinite;
+  animation-fill-mode: both;
+  height: 10px;
+  width: 10px;
+  background: #3b5998;;
+  position: absolute;
+  left:0;
+  top:0;
+  border-radius: 50%;
+}
+
+.typing .dot1 {
+  animation-delay: .2s;
+  margin-left: calc(10px * 1.5);
+}
+
+.typing .dot2 {
+  animation-delay: .4s;
+  margin-left: calc(10px * 3);
+}
+
+@keyframes blink {
+  0% {
+    opacity: .1;
+  }
+  20% {
+    opacity: 1;
+  }
+  100% {
+    opacity: .1;
+  }
+}
+
+#chat-tab .generating {
+  display: none !important;
+}

docker-compose.yml

@@ -0,0 +1,33 @@
+version: "3.3"
+services:
+  text-generation-webui:
+    build:
+      context: .
+      args:
+        # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus
+        TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-7.5}
+        WEBUI_VERSION: ${WEBUI_VERSION:-HEAD}
+    env_file: .env
+    ports:
+      - "${HOST_PORT:-7860}:${CONTAINER_PORT:-7860}"
+      - "${HOST_API_PORT:-5000}:${CONTAINER_API_PORT:-5000}"
+      - "${HOST_API_STREAM_PORT:-5005}:${CONTAINER_API_STREAM_PORT:-5005}"
+    stdin_open: true
+    tty: true
+    volumes:
+      - ./characters:/app/characters
+      - ./extensions:/app/extensions
+      - ./loras:/app/loras
+      - ./models:/app/models
+      - ./presets:/app/presets
+      - ./prompts:/app/prompts
+      - ./softprompts:/app/softprompts
+      - ./training:/app/training
+      - ./cloudflared:/etc/cloudflared
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [gpu]

docker/.dockerignore

@@ -0,0 +1,9 @@
+.env
+Dockerfile
+/characters
+/loras
+/models
+/presets
+/prompts
+/softprompts
+/training

docker/.env.example

@@ -0,0 +1,30 @@
+# by default the Dockerfile specifies these versions: 3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX
+# however for me to work i had to specify the exact version for my card ( 2060 ) it was 7.5
+# https://developer.nvidia.com/cuda-gpus you can find the version for your card here
+TORCH_CUDA_ARCH_LIST=7.5
+
+# these commands worked for me with roughly 4.5GB of vram
+CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices
+
+# the following examples have been tested with the files linked in docs/README_docker.md:
+# example running 13b with 4bit/128 groupsize        : CLI_ARGS=--model llama-13b-4bit-128g --wbits 4 --listen --groupsize 128 --pre_layer 25
+# example with loading api extension and public share: CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices --no-stream --extensions api --share
+# example running 7b with 8bit groupsize             : CLI_ARGS=--model llama-7b --load-in-8bit --listen --auto-devices
+
+# the port the webui binds to on the host
+HOST_PORT=7860
+# the port the webui binds to inside the container
+CONTAINER_PORT=7860
+
+# the port the api binds to on the host
+HOST_API_PORT=5000
+# the port the api binds to inside the container
+CONTAINER_API_PORT=5000
+
+# the port the api stream endpoint binds to on the host
+HOST_API_STREAM_PORT=5005
+# the port the api stream endpoint binds to inside the container
+CONTAINER_API_STREAM_PORT=5005
+
+# the version used to install text-generation-webui from
+WEBUI_VERSION=HEAD

docker/Dockerfile

@@ -0,0 +1,72 @@
+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa /build
+
+WORKDIR /build
+
+RUN python3 -m venv /build/venv
+RUN . /build/venv/bin/activate && \
+    pip3 install --upgrade pip setuptools wheel && \
+    pip3 install torch torchvision torchaudio && \
+    pip3 install -r requirements.txt
+
+# https://developer.nvidia.com/cuda-gpus
+# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5"
+ARG TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX}"
+RUN . /build/venv/bin/activate && \
+    python3 setup_cuda.py bdist_wheel -d .
+
+FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04
+
+LABEL maintainer="Your Name <your.email@example.com>"
+LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y python3-dev libportaudio2 libasound-dev git python3 python3-pip make g++ ffmpeg && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
+RUN mkdir /app
+
+WORKDIR /app
+
+ARG WEBUI_VERSION
+RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source"
+
+RUN virtualenv /app/venv
+RUN . /app/venv/bin/activate && \
+    pip3 install --upgrade pip setuptools wheel && \
+    pip3 install torch torchvision torchaudio
+
+COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa
+RUN . /app/venv/bin/activate && \
+    pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl
+
+COPY extensions/api/requirements.txt /app/extensions/api/requirements.txt
+COPY extensions/elevenlabs_tts/requirements.txt /app/extensions/elevenlabs_tts/requirements.txt
+COPY extensions/google_translate/requirements.txt /app/extensions/google_translate/requirements.txt
+COPY extensions/silero_tts/requirements.txt /app/extensions/silero_tts/requirements.txt
+COPY extensions/whisper_stt/requirements.txt /app/extensions/whisper_stt/requirements.txt
+COPY extensions/superbooga/requirements.txt /app/extensions/superbooga/requirements.txt
+COPY extensions/openai/requirements.txt /app/extensions/openai/requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/superbooga && pip3 install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/openai && pip3 install -r requirements.txt
+
+COPY requirements.txt /app/requirements.txt
+RUN . /app/venv/bin/activate && \
+    pip3 install -r requirements.txt
+
+RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
+
+COPY . /app/
+ENV CLI_ARGS=""
+CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS}

docker/docker-compose.yml

@@ -0,0 +1,33 @@
+version: "3.3"
+services:
+  text-generation-webui:
+    build:
+      context: .
+      args:
+        # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus
+        TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-7.5}
+        WEBUI_VERSION: ${WEBUI_VERSION:-HEAD}
+    env_file: .env
+    ports:
+      - "${HOST_PORT:-7860}:${CONTAINER_PORT:-7860}"
+      - "${HOST_API_PORT:-5000}:${CONTAINER_API_PORT:-5000}"
+      - "${HOST_API_STREAM_PORT:-5005}:${CONTAINER_API_STREAM_PORT:-5005}"
+    stdin_open: true
+    tty: true
+    volumes:
+      - ./characters:/app/characters
+      - ./extensions:/app/extensions
+      - ./loras:/app/loras
+      - ./models:/app/models
+      - ./presets:/app/presets
+      - ./prompts:/app/prompts
+      - ./softprompts:/app/softprompts
+      - ./training:/app/training
+      - ./cloudflared:/etc/cloudflared
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [gpu]

docs/Audio-Notification.md

@@ -0,0 +1,14 @@
+# Audio notification
+
+If your computer takes a long time to generate each response for the model that you are using, you can enable an audio notification for when the response is completed. This feature was kindly contributed by HappyWorldGames in [#1277](https://github.com/oobabooga/text-generation-webui/pull/1277).
+
+### Installation
+
+Simply place a file called "notification.mp3" in the same folder as `server.py`. Here you can find some examples:
+
+* https://pixabay.com/sound-effects/search/ding/?duration=0-30
+* https://pixabay.com/sound-effects/search/notification/?duration=0-30
+
+Source: https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1126
+
+This file will be automatically detected the next time you start the web UI.

docs/Chat-mode.md

@@ -0,0 +1,39 @@
+## Chat characters
+
+Custom chat mode characters are defined by `.yaml` files inside the `characters` folder. An example is included: [Example.yaml](https://github.com/oobabooga/text-generation-webui/blob/main/characters/Example.yaml).
+
+The following fields may be defined:
+
+| Field | Description |
+|-------|-------------|
+| `name` or `bot` | The character's name. |
+| `context` | A string that appears at the top of the prompt. It usually contains a description of the character's personality and a few example messages. |
+| `greeting` (optional) | The character's opening message. It appears when the character is first loaded or when the history is cleared. |
+| `your_name` or `user` (optional) | Your name. This overwrites what you had previously written in the `Your name` field in the interface. |
+
+#### Special tokens
+
+The following replacements happen when the prompt is generated, and they apply to the `context` and `greeting` fields:
+
+* `{{char}}` and `<BOT>` get replaced with the character's name.
+* `{{user}}` and `<USER>` get replaced with your name.
+
+#### How do I add a profile picture for my character?
+
+Put an image with the same name as your character's `.yaml` file into the `characters` folder. For example, if your bot is `Character.yaml`, add `Character.jpg` or `Character.png` to the folder.
+
+#### Is the chat history truncated in the prompt?
+
+Once your prompt reaches the `truncation_length` parameter (2048 by default), old messages will be removed one at a time. The context string will always stay at the top of the prompt and will never get truncated.
+
+## Chat styles
+
+Custom chat styles can be defined in the `text-generation-webui/css` folder. Simply create a new file with name starting in `chat_style-` and ending in `.css` and it will automatically appear in the "Chat style" dropdown menu in the interface. Examples:
+
+```
+chat_style-cai-chat.css
+chat_style-TheEncrypted777.css
+chat_style-wpp.css
+```
+
+You should use the same class names as in `chat_style-cai-chat.css` in your custom style.

docs/DeepSpeed.md

@@ -0,0 +1,24 @@
+An alternative way of reducing the GPU memory usage of models is to use the `DeepSpeed ZeRO-3` optimization.
+
+With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`.
+
+As far as I know, DeepSpeed is only available for Linux at the moment.
+
+### How to use it
+
+1. Install DeepSpeed: 
+
+```
+conda install -c conda-forge mpi4py mpich
+pip install -U deepspeed
+```
+
+2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example:
+
+```
+deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B
+```
+
+### Learn more
+
+For more information, check out [this comment](https://github.com/oobabooga/text-generation-webui/issues/40#issuecomment-1412038622) by 81300, who came up with the DeepSpeed support in this web UI.

docs/Docker.md

@@ -0,0 +1,203 @@
+Docker Compose is a way of installing and launching the web UI in an isolated Ubuntu image using only a few commands.
+
+In order to create the image as described in the main README, you must have docker compose 2.17 or higher:
+
+```
+~$ docker compose version
+Docker Compose version v2.17.2
+```
+
+Make sure to also create the necessary symbolic links:
+
+```
+cd text-generation-webui
+ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
+cp docker/.env.example .env
+# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
+docker compose up --build
+```
+
+# Table of contents
+
+* [Docker Compose installation instructions](#docker-compose-installation-instructions)
+* [Repository with additional Docker files](#dedicated-docker-repository)
+
+# Docker Compose installation instructions 
+
+By [@loeken](https://github.com/loeken).
+
+- [Ubuntu 22.04](#ubuntu-2204)
+  - [0. youtube video](#0-youtube-video)
+  - [1. update the drivers](#1-update-the-drivers)
+  - [2. reboot](#2-reboot)
+  - [3. install docker](#3-install-docker)
+  - [4. docker \& container toolkit](#4-docker--container-toolkit)
+  - [5. clone the repo](#5-clone-the-repo)
+  - [6. prepare models](#6-prepare-models)
+  - [7. prepare .env file](#7-prepare-env-file)
+  - [8. startup docker container](#8-startup-docker-container)
+- [Manjaro](#manjaro)
+  - [update the drivers](#update-the-drivers)
+  - [reboot](#reboot)
+  - [docker \& container toolkit](#docker--container-toolkit)
+  - [continue with ubuntu task](#continue-with-ubuntu-task)
+- [Windows](#windows)
+  - [0. youtube video](#0-youtube-video-1)
+  - [1. choco package manager](#1-choco-package-manager)
+  - [2. install drivers/dependencies](#2-install-driversdependencies)
+  - [3. install wsl](#3-install-wsl)
+  - [4. reboot](#4-reboot)
+  - [5. git clone \&\& startup](#5-git-clone--startup)
+  - [6. prepare models](#6-prepare-models-1)
+  - [7. startup](#7-startup)
+- [notes](#notes)
+
+## Ubuntu 22.04
+
+### 0. youtube video
+A video walking you through the setup can be found here:
+
+[![oobabooga text-generation-webui setup in docker on ubuntu 22.04](https://img.youtube.com/vi/ELkKWYh8qOk/0.jpg)](https://www.youtube.com/watch?v=ELkKWYh8qOk)
+
+
+### 1. update the drivers
+in the the “software updater” update drivers to the last version of the prop driver.
+
+### 2. reboot
+to switch using to new driver
+
+### 3. install docker
+```bash
+sudo apt update
+sudo apt-get install curl
+sudo mkdir -m 0755 -p /etc/apt/keyrings
+curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
+echo \
+  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
+  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
+  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
+sudo apt update
+sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-compose -y
+sudo usermod -aG docker $USER
+newgrp docker
+```
+
+### 4. docker & container toolkit
+```bash
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+echo "deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/amd64 /" | \
+sudo tee /etc/apt/sources.list.d/nvidia.list > /dev/null 
+sudo apt update
+sudo apt install nvidia-docker2 nvidia-container-runtime -y
+sudo systemctl restart docker
+```
+
+### 5. clone the repo
+```
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+```
+
+### 6. prepare models
+download and place the models inside the models folder. tested with:
+
+4bit
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
+
+8bit:
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
+
+### 7. prepare .env file
+edit .env values to your needs.
+```bash
+cp .env.example .env
+nano .env
+```
+
+### 8. startup docker container
+```bash
+docker compose up --build
+```
+
+## Manjaro
+manjaro/arch is similar to ubuntu just the dependency installation is more convenient
+
+### update the drivers
+```bash
+sudo mhwd -a pci nonfree 0300
+```
+### reboot
+```bash
+reboot
+```
+### docker & container toolkit
+```bash
+yay -S docker docker-compose buildkit gcc nvidia-docker
+sudo usermod -aG docker $USER
+newgrp docker
+sudo systemctl restart docker # required by nvidia-container-runtime
+```
+
+### continue with ubuntu task
+continue at [5. clone the repo](#5-clone-the-repo)
+
+## Windows
+### 0. youtube video
+A video walking you through the setup can be found here:
+[![oobabooga text-generation-webui setup in docker on windows 11](https://img.youtube.com/vi/ejH4w5b5kFQ/0.jpg)](https://www.youtube.com/watch?v=ejH4w5b5kFQ)
+
+### 1. choco package manager
+install package manager  (https://chocolatey.org/ )
+```
+Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
+```
+
+### 2. install drivers/dependencies
+```
+choco install nvidia-display-driver cuda git docker-desktop
+```
+
+### 3. install wsl
+wsl --install
+
+### 4. reboot
+after reboot enter username/password in wsl
+
+### 5. git clone && startup
+clone the repo and edit .env values to your needs.
+```
+cd Desktop
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+COPY .env.example .env
+notepad .env
+```
+
+### 6. prepare models
+download and place the models inside the models folder. tested with:
+
+4bit https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
+
+8bit: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
+
+### 7. startup
+```
+docker compose up
+```
+
+## notes
+
+on older ubuntus you can manually install the docker compose plugin like this:
+```
+DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker}
+mkdir -p $DOCKER_CONFIG/cli-plugins
+curl -SL https://github.com/docker/compose/releases/download/v2.17.2/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
+chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
+export PATH="$HOME/.docker/cli-plugins:$PATH"
+```
+
+# Dedicated docker repository
+
+An external repository maintains a docker wrapper for this project as well as several pre-configured 'one-click' `docker compose` variants (e.g., updated branches of GPTQ). It can be found at: [Atinoda/text-generation-webui-docker](https://github.com/Atinoda/text-generation-webui-docker).
+

docs/ExLlama.md

@@ -0,0 +1,22 @@
+# ExLlama
+
+### About
+
+ExLlama is an extremely optimized GPTQ backend for LLaMA models. It features much lower VRAM usage and much higher speeds due to not relying on unoptimized transformers code.
+
+### Usage
+
+Configure text-generation-webui to use exllama via the UI or command line:
+   - In the "Model" tab, set "Loader" to "exllama"
+   - Specify `--loader exllama` on the command line
+
+### Manual setup
+
+No additional installation steps are necessary since an exllama package is already included in the requirements.txt. If this package fails to install for some reason, you can install it manually by cloning the original repository into your `repositories/` folder:
+
+```
+mkdir repositories
+cd repositories
+git clone https://github.com/turboderp/exllama
+```
+

docs/Extensions.md

@@ -0,0 +1,244 @@
+# Extensions
+
+Extensions are defined by files named `script.py` inside subfolders of `text-generation-webui/extensions`. They are loaded at startup if the folder name is specified after the `--extensions` flag.
+
+For instance, `extensions/silero_tts/script.py` gets loaded with `python server.py --extensions silero_tts`.
+
+## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions)
+
+The repository above contains a directory of user extensions.
+
+If you create an extension, you are welcome to host it in a GitHub repository and submit a PR adding it to the list.
+
+## Built-in extensions
+
+|Extension|Description|
+|---------|-----------|
+|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` port 5000. This is the main API for the webui. |
+|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. |
+|[multimodal](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal) | Adds multimodality support (text+images). For a detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal/README.md) in the extension directory. |
+|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.|
+|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, responses are replaced with an audio widget. |
+|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. |
+|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. |
+|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). |
+|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that adds a hidden string at the beginning of the bot's reply in chat mode. |
+|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. |
+|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. |
+|[superbooga](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/superbooga)| An extension that uses ChromaDB to create an arbitrarily large pseudocontext, taking as input text files, URLs, or pasted text. Based on https://github.com/kaiokendev/superbig. |
+|[ngrok](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/ngrok)| Allows you to access the web UI remotely using the ngrok reverse tunnel service (free). It's an alternative to the built-in Gradio `--share` feature. |
+|[perplexity_colors](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/perplexity_colors)| Colors each token in the output text by its associated probability, as derived from the model logits. |
+
+## How to write an extension
+
+The extensions framework is based on special functions and variables that you can define in `script.py`. The functions are the following:
+
+| Function        | Description |
+|-------------|-------------|
+| `def setup()` | Is executed when the extension gets imported. |
+| `def ui()` | Creates custom gradio elements when the UI is launched. | 
+| `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. |
+| `def custom_js()` | Same as above but for javascript. |
+| `def input_modifier(string, state, is_chat=False)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
+| `def output_modifier(string, state, is_chat=False)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
+| `def chat_input_modifier(text, visible_text, state)` | Modifies both the visible and internal inputs in chat mode. Can be used to hijack the chat input with custom content. |
+| `def bot_prefix_modifier(string, state)`  | Applied in chat mode to the prefix for the bot's reply. |
+| `def state_modifier(state)`  | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. |
+| `def history_modifier(history)`  | Modifies the chat history before the text generation in chat mode begins. |
+| `def custom_generate_reply(...)` | Overrides the main text generation function. |
+| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
+| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. |
+| `def custom_tokenized_length(prompt)` | Used in conjunction with `tokenizer_modifier`, returns the length in tokens of `prompt`. See the `multimodal` extension for an example. |
+
+Additionally, you can define a special `params` dictionary. In it, the `display_name` key is used to define the displayed name of the extension in the UI, and the `is_tab` key is used to define whether the extension should appear in a new tab. By default, extensions appear at the bottom of the "Text generation" tab.
+
+Example:
+
+```python
+params = {
+    "display_name": "Google Translate",
+    "is_tab": True,
+}
+```
+
+The `params` dict may also contain variables that you want to be customizable through a `settings.yaml` file. For instance, assuming the extension is in `extensions/google_translate`, the variable `language string` in
+
+```python
+params = {
+    "display_name": "Google Translate",
+    "is_tab": True,
+    "language string": "jp"
+}
+```
+
+can be customized by adding a key called `google_translate-language string` to `settings.yaml`:
+
+```python
+google_translate-language string: 'fr'
+``` 
+
+That is, the syntax for the key is `extension_name-variable_name`.
+
+## Using multiple extensions at the same time
+
+You can activate more than one extension at a time by providing their names separated by spaces after `--extensions`. The input, output, and bot prefix modifiers will be applied in the specified order. 
+
+Example:
+
+```
+python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate
+python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm
+```
+
+Do note, that for:
+- `custom_generate_chat_prompt`
+- `custom_generate_reply`
+- `custom_tokenized_length`
+
+only the first declaration encountered will be used and the rest will be ignored. 
+
+## A full example
+
+The source code below can be found at [extensions/example/script.py](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/example/script.py).
+
+```python
+"""
+An example of extension. It does nothing, but you can add transformations
+before the return statements to customize the webui behavior.
+
+Starting from history_modifier and ending in output_modifier, the
+functions are declared in the same order that they are called at
+generation time.
+"""
+
+import gradio as gr
+import torch
+from transformers import LogitsProcessor
+
+from modules import chat, shared
+from modules.text_generation import (
+    decode,
+    encode,
+    generate_reply,
+)
+
+params = {
+    "display_name": "Example Extension",
+    "is_tab": False,
+}
+
+class MyLogits(LogitsProcessor):
+    """
+    Manipulates the probabilities for the next token before it gets sampled.
+    Used in the logits_processor_modifier function below.
+    """
+    def __init__(self):
+        pass
+
+    def __call__(self, input_ids, scores):
+        # probs = torch.softmax(scores, dim=-1, dtype=torch.float)
+        # probs[0] /= probs[0].sum()
+        # scores = torch.log(probs / (1 - probs))
+        return scores
+
+def history_modifier(history):
+    """
+    Modifies the chat history.
+    Only used in chat mode.
+    """
+    return history
+
+def state_modifier(state):
+    """
+    Modifies the state variable, which is a dictionary containing the input
+    values in the UI like sliders and checkboxes.
+    """
+    return state
+
+def chat_input_modifier(text, visible_text, state):
+    """
+    Modifies the user input string in chat mode (visible_text).
+    You can also modify the internal representation of the user
+    input (text) to change how it will appear in the prompt.
+    """
+    return text, visible_text
+
+def input_modifier(string, state, is_chat=False):
+    """
+    In default/notebook modes, modifies the whole prompt.
+
+    In chat mode, it is the same as chat_input_modifier but only applied
+    to "text", here called "string", and not to "visible_text".
+    """
+    return string
+
+def bot_prefix_modifier(string, state):
+    """
+    Modifies the prefix for the next bot reply in chat mode.
+    By default, the prefix will be something like "Bot Name:".
+    """
+    return string
+
+def tokenizer_modifier(state, prompt, input_ids, input_embeds):
+    """
+    Modifies the input ids and embeds.
+    Used by the multimodal extension to put image embeddings in the prompt.
+    Only used by loaders that use the transformers library for sampling.
+    """
+    return prompt, input_ids, input_embeds
+
+def logits_processor_modifier(processor_list, input_ids):
+    """
+    Adds logits processors to the list, allowing you to access and modify
+    the next token probabilities.
+    Only used by loaders that use the transformers library for sampling.
+    """
+    processor_list.append(MyLogits())
+    return processor_list
+
+def output_modifier(string, state, is_chat=False):
+    """
+    Modifies the LLM output before it gets presented.
+
+    In chat mode, the modified version goes into history['visible'],
+    and the original version goes into history['internal'].
+    """
+    return string
+
+def custom_generate_chat_prompt(user_input, state, **kwargs):
+    """
+    Replaces the function that generates the prompt from the chat history.
+    Only used in chat mode.
+    """
+    result = chat.generate_chat_prompt(user_input, state, **kwargs)
+    return result
+
+def custom_css():
+    """
+    Returns a CSS string that gets appended to the CSS for the webui.
+    """
+    return ''
+
+def custom_js():
+    """
+    Returns a javascript string that gets appended to the javascript
+    for the webui.
+    """
+    return ''
+
+def setup():
+    """
+    Gets executed only once, when the extension is imported.
+    """
+    pass
+
+def ui():
+    """
+    Gets executed when the UI is drawn. Custom gradio elements and
+    their corresponding event handlers should be defined here.
+
+    To learn about gradio components, check out the docs:
+    https://gradio.app/docs/
+    """
+    pass
+```

docs/GPTQ-models-(4-bit-mode).md

@@ -0,0 +1,187 @@
+GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
+
+4-bit GPTQ models reduce VRAM usage by about 75%. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
+
+## Overview
+
+There are two ways of loading GPTQ models in the web UI at the moment:
+
+* Using AutoGPTQ:
+  * supports more models
+  * standardized (no need to guess any parameter)
+  * is a proper Python library
+  * ~no wheels are presently available so it requires manual compilation~
+  * supports loading both triton and cuda models
+
+* Using GPTQ-for-LLaMa directly:
+  * faster CPU offloading
+  * faster multi-GPU inference
+  * supports loading LoRAs using a monkey patch
+  * requires you to manually figure out the wbits/groupsize/model_type parameters for the model to be able to load it
+  * supports either only cuda or only triton depending on the branch
+
+For creating new quantizations, I recommend using AutoGPTQ: https://github.com/PanQiWei/AutoGPTQ
+
+## AutoGPTQ
+
+### Installation
+
+No additional steps are necessary as AutoGPTQ is already in the `requirements.txt` for the webui. If you still want or need to install it manually for whatever reason, these are the commands:
+
+```
+conda activate textgen
+git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
+pip install .
+```
+
+The last command requires `nvcc` to be installed (see the [instructions above](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-1-install-nvcc)).
+
+### Usage
+
+When you quantize a model using AutoGPTQ, a folder containing a filed called `quantize_config.json` will be generated. Place that folder inside your `models/` folder and load it with the `--autogptq` flag:
+
+```
+python server.py --autogptq --model model_name
+```
+
+Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
+
+### Offloading
+
+In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
+
+For CPU offloading:
+
+```
+python server.py --autogptq --gpu-memory 3000MiB --model model_name
+```
+
+For multi-GPU inference:
+
+```
+python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
+```
+
+### Using LoRAs with AutoGPTQ
+
+Works fine for a single LoRA.
+
+## GPTQ-for-LLaMa
+
+GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made possible by [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa): https://github.com/qwopqwop200/GPTQ-for-LLaMa
+
+A Python package containing both major CUDA versions of GPTQ-for-LLaMa is used to simplify installation and compatibility: https://github.com/jllllll/GPTQ-for-LLaMa-CUDA
+
+### Precompiled wheels
+
+Kindly provided by our friend jllllll: https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases
+
+Wheels are included in requirements.txt and are installed with the webui on supported systems.
+
+### Manual installation
+
+#### Step 1: install nvcc
+
+```
+conda activate textgen
+conda install cuda -c nvidia/label/cuda-11.7.1
+```
+
+The command above takes some 10 minutes to run and shows no progress bar or updates along the way.
+
+You are also going to need to have a C++ compiler installed. On Linux, `sudo apt install build-essential` or equivalent is enough. On Windows, Visual Studio or Visual Studio Build Tools is required.
+
+If you're using an older version of CUDA toolkit (e.g. 11.7) but the latest version of `gcc` and `g++` (12.0+) on Linux, you should downgrade with: `conda install -c conda-forge gxx==11.3.0`. Kernel compilation will fail otherwise.
+
+#### Step 2: compile the CUDA extensions
+
+```
+python -m pip install git+https://github.com/jllllll/GPTQ-for-LLaMa-CUDA -v
+```
+
+### Getting pre-converted LLaMA weights
+
+* Direct download (recommended):
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-7B-4bit-128g
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-13B-4bit-128g
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-30B-4bit-128g
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-65B-4bit-128g
+
+These models were converted with `desc_act=True`. They work just fine with ExLlama. For AutoGPTQ, they will only work on Linux with the `triton` option checked.
+
+* Torrent:
+
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
+
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
+
+These models were converted with `desc_act=False`. As such, they are less accurate, but they work with AutoGPTQ on Windows. The `128g` versions are better from 13b upwards, and worse for 7b. The tokenizer files in the torrents are outdated, in particular the files called `tokenizer_config.json` and `special_tokens_map.json`. Here you can find those files: https://huggingface.co/oobabooga/llama-tokenizer
+
+### Starting the web UI:
+
+Use the `--gptq-for-llama` flag.
+
+For the models converted without `group-size`:
+
+```
+python server.py --model llama-7b-4bit --gptq-for-llama 
+```
+
+For the models converted with `group-size`:
+
+```
+python server.py --model llama-13b-4bit-128g  --gptq-for-llama --wbits 4 --groupsize 128
+```
+
+The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names in many cases.
+
+### CPU offloading
+
+It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU.
+
+With this command, I can run llama-7b with 4GB VRAM:
+
+```
+python server.py --model llama-7b-4bit --pre_layer 20
+```
+
+This is the performance:
+
+```
+Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)
+```
+
+You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of GPTQ, eg `--pre_layer 30 60` will load a LLaMA-30B model half onto your first GPU and half onto your second, or `--pre_layer 20 40` will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU.
+
+### Using LoRAs with GPTQ-for-LLaMa
+
+This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
+
+To use it:
+
+1. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
+
+```
+cd text-generation-webui/repositories
+git clone https://github.com/johnsmith0031/alpaca_lora_4bit
+```
+
+⚠️  I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0`
+
+2. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
+
+```
+pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
+```
+
+3. Start the UI with the `--monkey-patch` flag:
+
+```
+python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
+```
+
+

docs/LLaMA-model.md

@@ -0,0 +1,56 @@
+LLaMA is a Large Language Model developed by Meta AI. 
+
+It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.
+
+This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode)
+](GPTQ-models-(4-bit-mode).md).
+
+## Getting the weights
+
+### Option 1: pre-converted weights
+
+* Direct download (recommended):
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-7B-HF
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-13B-HF
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-30B-HF
+
+https://huggingface.co/Neko-Institute-of-Science/LLaMA-65B-HF
+
+* Torrent:
+
+https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
+
+The tokenizer files in the torrent above are outdated, in particular the files called `tokenizer_config.json` and `special_tokens_map.json`. Here you can find those files: https://huggingface.co/oobabooga/llama-tokenizer
+
+### Option 2: convert the weights yourself
+
+1. Install the `protobuf` library:
+
+```
+pip install protobuf==3.20.1
+```
+
+2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link.
+
+If you have `transformers` installed in place:
+
+```
+python -m transformers.models.llama.convert_llama_weights_to_hf --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
+```
+
+Otherwise download [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) first and run:
+
+```
+python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
+```
+
+3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder.
+
+## Starting the web UI
+
+```python
+python server.py --model llama-7b
+```

docs/LLaMA-v2-model.md

@@ -0,0 +1,35 @@
+# LLaMA-v2
+
+To convert LLaMA-v2 from the `.pth` format provided by Meta to transformers format, follow the steps below:
+
+1) `cd` into your `llama` folder (the one containing `download.sh` and the models that you downloaded):
+
+```
+cd llama
+```
+
+2) Clone the transformers library:
+
+```
+git clone 'https://github.com/huggingface/transformers'
+
+```
+
+3) Create symbolic links from the downloaded folders to names that the conversion script can recognize:
+
+```
+ln -s llama-2-7b 7B
+ln -s llama-2-13b 13B
+```
+
+4) Do the conversions:
+
+```
+mkdir llama-2-7b-hf llama-2-13b-hf
+python ./transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir . --model_size 7B --output_dir llama-2-7b-hf --safe_serialization true
+python ./transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir . --model_size 13B --output_dir llama-2-13b-hf --safe_serialization true
+```
+
+5) Move the output folders inside `text-generation-webui/models`
+
+6) Have fun

docs/LoRA.md

@@ -0,0 +1,71 @@
+# LoRA
+
+LoRA (Low-Rank Adaptation) is an extremely powerful method for customizing a base model by training only a small number of parameters. They can be attached to models at runtime.
+
+For instance, a 50mb LoRA can teach LLaMA an entire new language, a given writing style, or give it instruction-following or chat abilities.
+
+This is the current state of LoRA integration in the web UI:
+
+|Loader | Status |
+|--------|------|
+| Transformers | Full support in 16-bit, `--load-in-8bit`, `--load-in-4bit`, and CPU modes. |
+| ExLlama | Single LoRA support. Fast to remove the LoRA afterwards. |
+| AutoGPTQ | Single LoRA support. Removing the LoRA requires reloading the entire model.|
+| GPTQ-for-LLaMa | Full support with the [monkey patch](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#using-loras-with-gptq-for-llama). |
+
+## Downloading a LoRA
+
+The download script can be used. For instance:
+
+```
+python download-model.py tloen/alpaca-lora-7b
+```
+
+The files will be saved to `loras/tloen_alpaca-lora-7b`.
+
+## Using the LoRA
+
+The `--lora` command-line flag can be used. Examples:
+
+```
+python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b
+python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit
+python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-4bit
+python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu
+```
+
+Instead of using the `--lora` command-line flag, you can also select the LoRA in the "Parameters" tab of the interface.
+
+## Prompt
+For the Alpaca LoRA in particular, the prompt must be formatted like this:
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+Write a Python script that generates text using the transformers library.
+### Response:
+```
+
+Sample output:
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+Write a Python script that generates text using the transformers library.
+### Response:
+
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
+texts = ["Hello world", "How are you"]
+for sentence in texts:
+sentence = tokenizer(sentence)
+print(f"Generated {len(sentence)} tokens from '{sentence}'")
+output = model(sentences=sentence).predict()
+print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}")
+```
+
+## Training a LoRA
+
+You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details.

docs/Low-VRAM-guide.md

@@ -0,0 +1,53 @@
+If you GPU is not large enough to fit a 16-bit model, try these in the following order:
+
+### Load the model in 8-bit mode
+
+```
+python server.py --load-in-8bit
+```
+
+### Load the model in 4-bit mode
+
+```
+python server.py --load-in-4bit
+```
+
+### Split the model across your GPU and CPU
+
+```
+python server.py --auto-devices
+```
+
+If you can load the model with this command but it runs out of memory when you try to generate text, try increasingly limiting the amount of memory allocated to the GPU until the error stops happening:
+
+```
+python server.py --auto-devices --gpu-memory 10
+python server.py --auto-devices --gpu-memory 9
+python server.py --auto-devices --gpu-memory 8
+...
+```
+
+where the number is in GiB.
+
+For finer control, you can also specify the unit in MiB explicitly:
+
+```
+python server.py --auto-devices --gpu-memory 8722MiB
+python server.py --auto-devices --gpu-memory 4725MiB
+python server.py --auto-devices --gpu-memory 3500MiB
+...
+```
+
+### Send layers to a disk cache
+
+As a desperate last measure, you can split the model across your GPU, CPU, and disk:
+
+```
+python server.py --auto-devices --disk
+```
+
+With this, I am able to load a 30b model into my RTX 3090, but it takes 10 seconds to generate 1 word.
+
+### DeepSpeed (experimental)
+
+An experimental alternative to all of the above is to use DeepSpeed: [guide](DeepSpeed.md).

docs/README.md

@@ -0,0 +1,21 @@
+# text-generation-webui documentation
+
+## Table of contents
+
+* [Audio Notification](Audio-Notification.md)
+* [Chat mode](Chat-mode.md)
+* [DeepSpeed](DeepSpeed.md)
+* [Docker](Docker.md)
+* [ExLlama](ExLlama.md)
+* [Extensions](Extensions.md)
+* [GPTQ models (4 bit mode)](GPTQ-models-(4-bit-mode).md)
+* [LLaMA model](LLaMA-model.md)
+* [llama.cpp](llama.cpp.md)
+* [LoRA](LoRA.md)
+* [Low VRAM guide](Low-VRAM-guide.md)
+* [RWKV model](RWKV-model.md)
+* [Spell book](Spell-book.md)
+* [System requirements](System-requirements.md)
+* [Training LoRAs](Training-LoRAs.md)
+* [Windows installation guide](Windows-installation-guide.md)
+* [WSL installation guide](WSL-installation-guide.md)

docs/RWKV-model.md

@@ -0,0 +1,72 @@
+> RWKV: RNN with Transformer-level LLM Performance
+>
+> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
+
+https://github.com/BlinkDL/RWKV-LM
+
+https://github.com/BlinkDL/ChatRWKV
+
+## Using RWKV in the web UI
+
+### Hugging Face weights
+
+Simply download the weights from https://huggingface.co/RWKV and load them as you would for any other model.
+
+There is a bug in transformers==4.29.2 that prevents RWKV from being loaded in 8-bit mode. You can install the dev branch to solve this bug: `pip install git+https://github.com/huggingface/transformers`
+
+### Original .pth weights
+
+The instructions below are from before RWKV was supported in transformers, and they are kept for legacy purposes. The old implementation is possibly faster, but it lacks the full range of samplers that the transformers library offers.
+
+#### 0. Install the RWKV library
+
+```
+pip install rwkv
+```
+
+`0.7.3` was the last version that I tested. If you experience any issues, try ```pip install rwkv==0.7.3```.
+
+#### 1. Download the model
+
+It is available in different sizes:
+
+* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/
+* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/
+* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/
+
+There are also older releases with smaller sizes like:
+
+* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth
+
+Download the chosen `.pth` and put it directly in the `models` folder. 
+
+#### 2. Download the tokenizer
+
+[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json)
+
+Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`.
+
+#### 3. Launch the web UI
+
+No additional steps are required. Just launch it as you would with any other model.
+
+```
+python server.py --listen  --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth
+```
+
+#### Setting a custom strategy
+
+It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include:
+
+```
+"cpu fp32" # CPU mode
+"cuda fp16" # GPU mode with float16 precision
+"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation.
+"cuda fp16i8" # GPU mode with 8-bit precision
+```
+
+See the README for the PyPl package for more details: https://pypi.org/project/rwkv/
+
+#### Compiling the CUDA kernel
+
+You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet.

docs/Spell-book.md

@@ -0,0 +1,107 @@
+You have now entered a hidden corner of the internet.
+
+A confusing yet intriguing realm of paradoxes and contradictions.
+
+A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along.
+
+![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg)
+
+*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.*
+
+#### You can train LoRAs in CPU mode
+
+Load the web UI with
+
+```
+python server.py --cpu
+```
+
+and start training the LoRA from the training tab as usual.
+
+#### 8-bit mode works with CPU offloading
+
+```
+python server.py --load-in-8bit --gpu-memory 4000MiB
+```
+
+#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models
+
+```
+python server.py --wbits 4 --groupsize 128 --pre_layer 20
+```
+
+#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes
+
+```
+python server.py --cpu
+python server.py
+python server.py --load-in-8bit
+python server.py --wbits 4
+```
+
+#### The web UI works with any version of GPTQ-for-LLaMa
+
+Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time:
+
+```
+cd text-generation-webui/repositories
+rm -r GPTQ-for-LLaMa
+pip uninstall quant-cuda
+git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch
+cd GPTQ-for-LLaMa
+python setup_cuda.py install
+```
+
+#### Instruction-following templates are represented as chat characters
+
+https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following
+
+#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode
+
+Otherwise the prompt will not be formatted correctly.
+
+1. Start the web UI with
+
+```
+python server.py --chat
+```
+
+2. Click on the "instruct" option under "Chat modes"
+
+3. Select the correct template in the hidden dropdown menu that will become visible. 
+
+#### Notebook mode is best mode
+
+Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating.
+
+#### RWKV is a RNN 
+
+Most models are transformers, but not RWKV, which is a RNN. It's a great model.
+
+#### `--gpu-memory` is not a hard limit on the GPU memory
+
+It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory.
+
+#### Contrastive search perhaps the best preset
+
+But it uses a ton of VRAM.
+
+#### You can check the sha256sum of downloaded models with the download script
+
+```
+python download-model.py facebook/galactica-125m --check
+```
+
+#### The download script continues interrupted downloads by default
+
+It doesn't start over.
+
+#### You can download models with multiple threads
+
+```
+python download-model.py facebook/galactica-125m --threads 8
+```
+
+#### LoRAs work in 4-bit mode
+
+You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag.

docs/System-requirements.md

@@ -0,0 +1,42 @@
+These are the VRAM and RAM requirements (in MiB) to run some examples of models **in 16-bit (default) precision**:
+
+| model                  |   VRAM (GPU) |     RAM |
+|:-----------------------|-------------:|--------:|
+| arxiv_ai_gpt2          |      1512.37 | 5824.2  |
+| blenderbot-1B-distill  |      2441.75 | 4425.91 |
+| opt-1.3b               |      2509.61 | 4427.79 |
+| gpt-neo-1.3b           |      2605.27 | 5851.58 |
+| opt-2.7b               |      5058.05 | 4863.95 |
+| gpt4chan_model_float16 |     11653.7  | 4437.71 |
+| gpt-j-6B               |     11653.7  | 5633.79 |
+| galactica-6.7b         |     12697.9  | 4429.89 |
+| opt-6.7b               |     12700    | 4368.66 |
+| bloomz-7b1-p3          |     13483.1  | 4470.34 |
+
+#### GPU mode with 8-bit precision
+
+Allows you to load models that would not normally fit into your GPU. Enabled by default for 13b and 20b models in this web UI.
+
+| model          |   VRAM (GPU) |     RAM |
+|:---------------|-------------:|--------:|
+| opt-13b        |      12528.1 | 1152.39 |
+| gpt-neox-20b   |      20384   | 2291.7  |
+
+#### CPU mode (32-bit precision)
+
+A lot slower, but does not require a GPU. 
+
+On my i5-12400F, 6B models take around 10-20 seconds to respond in chat mode, and around 5 minutes to generate a 200 tokens completion. 
+
+| model                  |      RAM |
+|:-----------------------|---------:|
+| arxiv_ai_gpt2          |  4430.82 |
+| gpt-neo-1.3b           |  6089.31 |
+| opt-1.3b               |  8411.12 |
+| blenderbot-1B-distill  |  8508.16 |
+| opt-2.7b               | 14969.3  |
+| bloomz-7b1-p3          | 21371.2  |
+| gpt-j-6B               | 24200.3  |
+| gpt4chan_model         | 24246.3  |
+| galactica-6.7b         | 26561.4  |
+| opt-6.7b               | 29596.6  |

docs/Training-LoRAs.md

@@ -0,0 +1,174 @@
+## Training Your Own LoRAs
+
+The WebUI seeks to make training your own LoRAs as easy as possible. It comes down to just a few simple steps:
+
+### **Step 1**: Make a plan.
+- What base model do you want to use? The LoRA you make has to be matched up to a single architecture (eg LLaMA-13B) and cannot be transferred to others (eg LLaMA-7B, StableLM, etc. would all be different). Derivatives of the same model (eg Alpaca finetune of LLaMA-13B) might be transferrable, but even then it's best to train exactly on what you plan to use.
+- What model format do you want? At time of writing, 8-bit models are most stable, and 4-bit are supported but experimental. In the near future it is likely that 4-bit will be the best option for most users.
+- What are you training it on? Do you want it to learn real information, a simple format, ...?
+
+### **Step 2**: Gather a dataset.
+- If you use a dataset similar to the [Alpaca](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json) format, that is natively supported by the `Formatted Dataset` input in the WebUI, with premade formatter options.
+- If you use a dataset that isn't matched to Alpaca's format, but uses the same basic JSON structure, you can make your own format file by copying `training/formats/alpaca-format.json` to a new file and [editing its content](#format-files).
+- If you can get the dataset into a simple text file, that works too! You can train using the `Raw text file` input option.
+    - This means you can for example just copy/paste a chatlog/documentation page/whatever you want, shove it in a plain text file, and train on it.
+- If you use a structured dataset not in this format, you may have to find an external way to convert it - or open an issue to request native support.
+
+### **Step 3**: Do the training.
+- **3.1**: Load the WebUI, and your model.
+    - Make sure you don't have any LoRAs already loaded (unless you want to train for multi-LoRA usage).
+- **3.2**: Open the `Training` tab at the top, `Train LoRA` sub-tab.
+- **3.3**: Fill in the name of the LoRA, select your dataset in the dataset options.
+- **3.4**: Select other parameters to your preference. See [parameters below](#parameters).
+- **3.5**: click `Start LoRA Training`, and wait.
+    - It can take a few hours for a large dataset, or just a few minute if doing a small run.
+    - You may want to monitor your [loss value](#loss) while it goes.
+
+### **Step 4**: Evaluate your results.
+- Load the LoRA under the Models Tab.
+- You can go test-drive it on the `Text generation` tab, or you can use the `Perplexity evaluation` sub-tab of the `Training` tab.
+- If you used the `Save every n steps` option, you can grab prior copies of the model from sub-folders within the LoRA model's folder and try them instead.
+
+### **Step 5**: Re-run if you're unhappy.
+- Make sure to unload the LoRA before training it.
+- You can simply resume a prior run - use `Copy parameters from` to select your LoRA, and edit parameters. Note that you cannot change the `Rank` of an already created LoRA.
+    - If you want to resume from a checkpoint saved along the way, simply copy the contents of the checkpoint folder into the LoRA's folder.
+    - (Note: `adapter_model.bin` is the important file that holds the actual LoRA content).
+    - This will start Learning Rate and Steps back to the start. If you want to resume as if you were midway through, you can adjust your Learning Rate to the last reported LR in logs and reduce your epochs.
+- Or, you can start over entirely if you prefer.
+- If your model is producing corrupted outputs, you probably need to start over and use a lower Learning Rate.
+- If your model isn't learning detailed information but you want it to, you might need to just run more epochs, or you might need a higher Rank.
+- If your model is enforcing a format you didn't want, you may need to tweak your dataset, or start over and not train as far.
+
+## Format Files
+
+If using JSON formatted datasets, they are presumed to be in the following approximate format:
+
+```json
+[
+    {
+        "somekey": "somevalue",
+        "key2": "value2"
+    },
+    {
+        // etc
+    }
+]
+```
+
+Where the keys (eg `somekey`, `key2` above) are standardized, and relatively consistent across the dataset, and the values (eg `somevalue`, `value2`) contain the content actually intended to be trained.
+
+For Alpaca, the keys are `instruction`, `input`, and `output`, wherein `input` is sometimes blank.
+
+A simple format file for Alpaca to be used as a chat bot is:
+
+```json
+{
+    "instruction,output": "User: %instruction%\nAssistant: %output%",
+    "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%"
+}
+```
+
+Note that the keys (eg `instruction,output`) are a comma-separated list of dataset keys, and the values are a simple string that use those keys with `%%`.
+
+So for example if a dataset has `"instruction": "answer my question"`, then the format file's `User: %instruction%\n` will be automatically filled in as `User: answer my question\n`.
+
+If you have different sets of key inputs, you can make your own format file to match it. This format-file is designed to be as simple as possible to enable easy editing to match your needs.
+
+## Raw Text File Settings
+
+When using raw text files as your dataset, the text is automatically split into chunks based on your `Cutoff Length` you get a few basic options to configure them.
+- `Overlap Length` is how much to overlap chunks by. Overlapping chunks helps prevent the model from learning strange mid-sentence cuts, and instead learn continual sentences that flow from earlier text.
+- `Prefer Newline Cut Length` sets a maximum distance in characters to shift the chunk cut towards newlines. Doing this helps prevent lines from starting or ending mid-sentence, preventing the model from learning to cut off sentences randomly.
+- `Hard Cut String` sets a string that indicates there must be a hard cut without overlap. This defaults to `\n\n\n`, meaning 3 newlines. No trained chunk will ever contain this string. This allows you to insert unrelated sections of text in the same text file, but still ensure the model won't be taught to randomly change the subject.
+
+## Parameters
+
+The basic purpose and function of each parameter is documented on-page in the WebUI, so read through them in the UI to understand your options.
+
+That said, here's a guide to the most important parameter choices you should consider:
+
+### VRAM
+
+- First, you must consider your VRAM availability.
+    - Generally, under default settings, VRAM usage for training with default parameters is very close to when generating text (with 1000+ tokens of context) (ie, if you can generate text, you can train LoRAs).
+        - Note: worse by default in the 4-bit monkeypatch currently. Reduce `Micro Batch Size` to `1` to restore this to expectations.
+    - If you have VRAM to spare, setting higher batch sizes will use more VRAM and get you better quality training in exchange.
+    - If you have large data, setting a higher cutoff length may be beneficial, but will cost significant VRAM. If you can spare some, set your batch size to `1` and see how high you can push your cutoff length.
+    - If you're low on VRAM, reducing batch size or cutoff length will of course improve that.
+    - Don't be afraid to just try it and see what happens. If it's too much, it will just error out, and you can lower settings and try again.
+
+### Rank
+
+- Second, you want to consider the amount of learning you want.
+    - For example, you may wish to just learn a dialogue format (as in the case of Alpaca) in which case setting a low `Rank` value (32 or lower) works great.
+    - Or, you might be training on project documentation you want the bot to understand and be able to understand questions about, in which case the higher the rank, the better.
+    - Generally, higher Rank = more precise learning = more total content learned = more VRAM usage while training.
+
+### Learning Rate and Epochs
+
+- Third, how carefully you want it to be learned.
+    - In other words, how okay or not you are with the model losing unrelated understandings.
+    - You can control this with 3 key settings: the Learning Rate, its scheduler, and your total epochs.
+    - The learning rate controls how much change is made to the model by each token it sees.
+        - It's in scientific notation normally, so for example `3e-4` means `3 * 10^-4` which is `0.0003`. The number after `e-` controls how many `0`s are in the number.
+        - Higher values let training run faster, but also are more likely to corrupt prior data in the model.
+    - You essentially have two variables to balance: the LR, and Epochs.
+        - If you make LR higher, you can set Epochs equally lower to match. High LR + low epochs = very fast, low quality training.
+        - If you make LR low, set epochs high. Low LR + high epochs = slow but high-quality training.
+    - The scheduler controls change-over-time as you train - it starts high, and then goes low. This helps balance getting data in, and having decent quality, at the same time.
+        - You can see graphs of the different scheduler options [in the HuggingFace docs here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_1/en/main_classes/optimizer_schedules#transformers.SchedulerType)
+
+## Loss
+
+When you're running training, the WebUI's console window will log reports that include, among other things, a numeric value named `Loss`. It will start as a high number, and gradually get lower and lower as it goes.
+
+"Loss" in the world of AI training theoretically means "how close is the model to perfect", with `0` meaning "absolutely perfect". This is calculated by measuring the difference between the model outputting exactly the text you're training it to output, and what it actually outputs.
+
+In practice, a good LLM should have a very complex variable range of ideas running in its artificial head, so a loss of `0` would indicate that the model has broken and forgotten to how think about anything other than what you trained it.
+
+So, in effect, Loss is a balancing game: you want to get it low enough that it understands your data, but high enough that it isn't forgetting everything else. Generally, if it goes below `1.0`, it's going to start forgetting its prior memories, and you should stop training. In some cases you may prefer to take it as low as `0.5` (if you want it to be very very predictable). Different goals have different needs, so don't be afraid to experiment and see what works best for you.
+
+Note: if you see Loss start at or suddenly jump to exactly `0`, it is likely something has gone wrong in your training process (eg model corruption).
+
+## Note: 4-Bit Monkeypatch
+
+The [4-bit LoRA monkeypatch](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) works for training, but has side effects:
+- VRAM usage is higher currently. You can reduce the `Micro Batch Size` to `1` to compensate.
+- Models do funky things. LoRAs apply themselves, or refuse to apply, or spontaneously error out, or etc. It can be helpful to reload base model or restart the WebUI between training/usage to minimize chances of anything going haywire.
+- Loading or working with multiple LoRAs at the same time doesn't currently work.
+- Generally, recognize and treat the monkeypatch as the dirty temporary hack it is - it works, but isn't very stable. It will get better in time when everything is merged upstream for full official support.
+
+## Legacy notes
+
+LoRA training was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570).
+
+### Using the original alpaca-lora code
+
+Kept here for reference. The Training tab has much more features than this method.
+
+```
+conda activate textgen
+git clone https://github.com/tloen/alpaca-lora
+```
+
+Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda:
+
+```
+model = LlamaForCausalLM.from_pretrained(
+    "models/llama-7b",
+    load_in_8bit=True,
+    device_map="auto",
+)
+tokenizer = LlamaTokenizer.from_pretrained(
+    "models/llama-7b", add_eos_token=True
+)
+```
+
+Run the script with:
+
+```
+python finetune.py
+```
+
+It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode).

docs/WSL-installation-guide.md

@@ -0,0 +1,82 @@
+Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton.
+
+-----
+
+Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11:
+
+## Step 1: Enable WSL
+
+1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges.
+2. In the PowerShell window, type the following command and press Enter:
+
+```
+wsl --install
+```
+
+If this command doesn't work, you can enable WSL with the following command for Windows 10:
+
+```
+wsl --set-default-version 1
+```
+
+For Windows 11, you can use:
+
+```
+wsl --set-default-version 2
+```
+
+You may be prompted to restart your computer. If so, save your work and restart.
+
+## Step 2: Install Ubuntu
+
+1. Open the Microsoft Store.
+2. Search for "Ubuntu" in the search bar.
+3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app.
+4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app.
+
+## Step 3: Set up Ubuntu
+
+1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment.
+2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment.
+
+## Step 4: Update and upgrade packages
+
+1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal:
+
+```
+sudo apt update
+sudo apt upgrade
+```
+
+2. Enter your password when prompted. This will update the package list and upgrade any outdated packages.
+
+Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files.
+
+You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal.
+
+## Step 5: Proceed with Linux instructions
+
+1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt:
+
+```
+sudo apt install [missing package]
+```
+
+You will probably need to install build-essential
+
+```
+sudo apt install build-essential
+```
+
+If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/
+
+#### WSL2 performance using /mnt: 
+when you git clone a repository, put it inside WSL and not outside. To understand more, take a look at this [issue](https://github.com/microsoft/WSL/issues/4197#issuecomment-604592340)
+
+## Bonus: Port Forwarding
+
+By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). 
+
+```
+netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860
+```

docs/Windows-installation-guide.md

@@ -0,0 +1,9 @@
+If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering:
+
+* Windows installation
+* 8-bit mode on Windows
+* LLaMA
+* LLaMA 4-bit
+
+The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/
+

docs/llama.cpp.md

@@ -0,0 +1,45 @@
+# llama.cpp
+
+llama.cpp is the best backend in two important scenarios:
+
+1) You don't have a GPU.
+2) You want to run a model that doesn't fit into your GPU.
+
+## Setting up the models
+
+#### Pre-converted
+
+Download the GGUF or GGML models directly into your `text-generation-webui/models` folder. It will be a single file.
+
+* For GGUF models, make sure its name contains `.gguf`.
+* For GGML models, make sure its name contains `ggml` and ends in `.bin`.
+
+`q4_K_M` quantization is recommended.
+
+#### Convert Llama yourself
+
+Follow the instructions in the llama.cpp README to generate a ggml: https://github.com/ggerganov/llama.cpp#prepare-data--run
+
+## GPU acceleration
+
+Enabled with the `--n-gpu-layers` parameter. 
+
+* If you have enough VRAM, use a high number like `--n-gpu-layers 1000` to offload all layers to the GPU. 
+* Otherwise, start with a low number like `--n-gpu-layers 10` and then gradually increase it until you run out of memory.
+
+This feature works out of the box for NVIDIA GPUs on Linux (amd64) or Windows. For other GPUs, you need to uninstall `llama-cpp-python` with
+
+```
+pip uninstall -y llama-cpp-python
+```
+
+and then recompile it using the commands here: https://pypi.org/project/llama-cpp-python/
+
+#### macOS
+
+For macOS, these are the commands:
+
+```
+pip uninstall -y llama-cpp-python
+CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir
+```

download-model.py

@@ -0,0 +1,275 @@
+'''
+Downloads models from Hugging Face to models/username_modelname.
+
+Example:
+python download-model.py facebook/opt-1.3b
+
+'''
+
+import argparse
+import base64
+import datetime
+import hashlib
+import json
+import os
+import re
+import sys
+from pathlib import Path
+
+import requests
+import tqdm
+from requests.adapters import HTTPAdapter
+from tqdm.contrib.concurrent import thread_map
+
+
+class ModelDownloader:
+    def __init__(self, max_retries=5):
+        self.session = requests.Session()
+        if max_retries:
+            self.session.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
+            self.session.mount('https://huggingface.co', HTTPAdapter(max_retries=max_retries))
+        if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
+            self.session.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
+        if os.getenv('HF_TOKEN') is not None:
+            self.session.headers = {'authorization': f'Bearer {os.getenv("HF_TOKEN")}'}
+
+    def sanitize_model_and_branch_names(self, model, branch):
+        if model[-1] == '/':
+            model = model[:-1]
+
+        if branch is None:
+            branch = "main"
+        else:
+            pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
+            if not pattern.match(branch):
+                raise ValueError(
+                    "Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
+
+        return model, branch
+
+    def get_download_links_from_huggingface(self, model, branch, text_only=False, specific_file=None):
+        base = "https://huggingface.co"
+        page = f"/api/models/{model}/tree/{branch}"
+        cursor = b""
+
+        links = []
+        sha256 = []
+        classifications = []
+        has_pytorch = False
+        has_pt = False
+        has_gguf = False
+        has_ggml = False
+        has_safetensors = False
+        is_lora = False
+        while True:
+            url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
+            r = self.session.get(url, timeout=10)
+            r.raise_for_status()
+            content = r.content
+
+            dict = json.loads(content)
+            if len(dict) == 0:
+                break
+
+            for i in range(len(dict)):
+                fname = dict[i]['path']
+                if specific_file not in [None, ''] and fname != specific_file:
+                    continue
+
+                if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
+                    is_lora = True
+
+                is_pytorch = re.match(r"(pytorch|adapter|gptq)_model.*\.bin", fname)
+                is_safetensors = re.match(r".*\.safetensors", fname)
+                is_pt = re.match(r".*\.pt", fname)
+                is_gguf = re.match(r'.*\.gguf', fname)
+                is_ggml = re.match(r".*ggml.*\.bin", fname)
+                is_tokenizer = re.match(r"(tokenizer|ice|spiece).*\.model", fname)
+                is_text = re.match(r".*\.(txt|json|py|md)", fname) or is_tokenizer
+                if any((is_pytorch, is_safetensors, is_pt, is_gguf, is_ggml, is_tokenizer, is_text)):
+                    if 'lfs' in dict[i]:
+                        sha256.append([fname, dict[i]['lfs']['oid']])
+
+                    if is_text:
+                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
+                        classifications.append('text')
+                        continue
+
+                    if not text_only:
+                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
+                        if is_safetensors:
+                            has_safetensors = True
+                            classifications.append('safetensors')
+                        elif is_pytorch:
+                            has_pytorch = True
+                            classifications.append('pytorch')
+                        elif is_pt:
+                            has_pt = True
+                            classifications.append('pt')
+                        elif is_gguf:
+                            has_gguf = True
+                            classifications.append('gguf')
+                        elif is_ggml:
+                            has_ggml = True
+                            classifications.append('ggml')
+
+            cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
+            cursor = base64.b64encode(cursor)
+            cursor = cursor.replace(b'=', b'%3D')
+
+        # If both pytorch and safetensors are available, download safetensors only
+        if (has_pytorch or has_pt) and has_safetensors:
+            for i in range(len(classifications) - 1, -1, -1):
+                if classifications[i] in ['pytorch', 'pt']:
+                    links.pop(i)
+
+        # If both GGML and GGUF are available, download GGUF only
+        if has_ggml and has_gguf:
+            for i in range(len(classifications) - 1, -1, -1):
+                if classifications[i] == 'ggml':
+                    links.pop(i)
+
+        return links, sha256, is_lora, ((has_ggml or has_gguf) and specific_file is not None)
+
+    def get_output_folder(self, model, branch, is_lora, is_llamacpp=False, base_folder=None):
+        if base_folder is None:
+            base_folder = 'models' if not is_lora else 'loras'
+
+        # If the model is of type GGUF or GGML, save directly in the base_folder
+        if is_llamacpp:
+            return Path(base_folder)
+
+        output_folder = f"{'_'.join(model.split('/')[-2:])}"
+        if branch != 'main':
+            output_folder += f'_{branch}'
+
+        output_folder = Path(base_folder) / output_folder
+        return output_folder
+
+    def get_single_file(self, url, output_folder, start_from_scratch=False):
+        filename = Path(url.rsplit('/', 1)[1])
+        output_path = output_folder / filename
+        headers = {}
+        mode = 'wb'
+        if output_path.exists() and not start_from_scratch:
+
+            # Check if the file has already been downloaded completely
+            r = self.session.get(url, stream=True, timeout=10)
+            total_size = int(r.headers.get('content-length', 0))
+            if output_path.stat().st_size >= total_size:
+                return
+
+            # Otherwise, resume the download from where it left off
+            headers = {'Range': f'bytes={output_path.stat().st_size}-'}
+            mode = 'ab'
+
+        with self.session.get(url, stream=True, headers=headers, timeout=10) as r:
+            r.raise_for_status()  # Do not continue the download if the request was unsuccessful
+            total_size = int(r.headers.get('content-length', 0))
+            block_size = 1024 * 1024  # 1MB
+            with open(output_path, mode) as f:
+                with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
+                    count = 0
+                    for data in r.iter_content(block_size):
+                        t.update(len(data))
+                        f.write(data)
+                        if total_size != 0 and self.progress_bar is not None:
+                            count += len(data)
+                            self.progress_bar(float(count) / float(total_size), f"{filename}")
+
+    def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
+        thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
+
+    def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1, specific_file=None, is_llamacpp=False):
+        self.progress_bar = progress_bar
+
+        # Create the folder and writing the metadata
+        output_folder.mkdir(parents=True, exist_ok=True)
+
+        if not is_llamacpp:
+            metadata = f'url: https://huggingface.co/{model}\n' \
+                       f'branch: {branch}\n' \
+                       f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
+
+            sha256_str = '\n'.join([f'    {item[1]} {item[0]}' for item in sha256])
+            if sha256_str:
+                metadata += f'sha256sum:\n{sha256_str}'
+
+            metadata += '\n'
+            (output_folder / 'huggingface-metadata.txt').write_text(metadata)
+
+        if specific_file:
+            print(f"Downloading {specific_file} to {output_folder}")
+        else:
+            print(f"Downloading the model to {output_folder}")
+
+        self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
+
+    def check_model_files(self, model, branch, links, sha256, output_folder):
+        # Validate the checksums
+        validated = True
+        for i in range(len(sha256)):
+            fpath = (output_folder / sha256[i][0])
+
+            if not fpath.exists():
+                print(f"The following file is missing: {fpath}")
+                validated = False
+                continue
+
+            with open(output_folder / sha256[i][0], "rb") as f:
+                bytes = f.read()
+                file_hash = hashlib.sha256(bytes).hexdigest()
+                if file_hash != sha256[i][1]:
+                    print(f'Checksum failed: {sha256[i][0]}  {sha256[i][1]}')
+                    validated = False
+                else:
+                    print(f'Checksum validated: {sha256[i][0]}  {sha256[i][1]}')
+
+        if validated:
+            print('[+] Validated checksums of all model files!')
+        else:
+            print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('MODEL', type=str, default=None, nargs='?')
+    parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
+    parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
+    parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
+    parser.add_argument('--specific-file', type=str, default=None, help='Name of the specific file to download (if not provided, downloads all).')
+    parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
+    parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
+    parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
+    parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
+    args = parser.parse_args()
+
+    branch = args.branch
+    model = args.MODEL
+    specific_file = args.specific_file
+
+    if model is None:
+        print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
+        sys.exit()
+
+    downloader = ModelDownloader(max_retries=args.max_retries)
+    # Clean up the model/branch names
+    try:
+        model, branch = downloader.sanitize_model_and_branch_names(model, branch)
+    except ValueError as err_branch:
+        print(f"Error: {err_branch}")
+        sys.exit()
+
+    # Get the download links from Hugging Face
+    links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only, specific_file=specific_file)
+
+    # Get the output folder
+    output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp, base_folder=args.output)
+
+    if args.check:
+        # Check previously downloaded files
+        downloader.check_model_files(model, branch, links, sha256, output_folder)
+    else:
+        # Download files
+        downloader.download_model_files(model, branch, links, sha256, output_folder, specific_file=specific_file, threads=args.threads, is_llamacpp=is_llamacpp)

extensions/api/blocking_api.py

@@ -0,0 +1,224 @@
+import json
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from threading import Thread
+
+from extensions.api.util import build_parameters, try_start_cloudflared
+from modules import shared
+from modules.chat import generate_chat_reply
+from modules.LoRA import add_lora_to_model
+from modules.models import load_model, unload_model
+from modules.models_settings import (
+    get_model_settings_from_yamls,
+    update_model_parameters
+)
+from modules.text_generation import (
+    encode,
+    generate_reply,
+    stop_everything_event
+)
+from modules.utils import get_available_models
+
+
+def get_model_info():
+    return {
+        'model_name': shared.model_name,
+        'lora_names': shared.lora_names,
+        # dump
+        'shared.settings': shared.settings,
+        'shared.args': vars(shared.args),
+    }
+
+
+class Handler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        if self.path == '/api/v1/model':
+            self.send_response(200)
+            self.end_headers()
+            response = json.dumps({
+                'result': shared.model_name
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+        else:
+            self.send_error(404)
+
+    def do_POST(self):
+        content_length = int(self.headers['Content-Length'])
+        body = json.loads(self.rfile.read(content_length).decode('utf-8'))
+
+        if self.path == '/api/v1/generate':
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            prompt = body['prompt']
+            generate_params = build_parameters(body)
+            stopping_strings = generate_params.pop('stopping_strings')
+            generate_params['stream'] = False
+
+            generator = generate_reply(
+                prompt, generate_params, stopping_strings=stopping_strings, is_chat=False)
+
+            answer = ''
+            for a in generator:
+                answer = a
+
+            response = json.dumps({
+                'results': [{
+                    'text': answer
+                }]
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+
+        elif self.path == '/api/v1/chat':
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            user_input = body['user_input']
+            regenerate = body.get('regenerate', False)
+            _continue = body.get('_continue', False)
+
+            generate_params = build_parameters(body, chat=True)
+            generate_params['stream'] = False
+
+            generator = generate_chat_reply(
+                user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
+
+            answer = generate_params['history']
+            for a in generator:
+                answer = a
+
+            response = json.dumps({
+                'results': [{
+                    'history': answer
+                }]
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+
+        elif self.path == '/api/v1/stop-stream':
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            stop_everything_event()
+
+            response = json.dumps({
+                'results': 'success'
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+
+        elif self.path == '/api/v1/model':
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            # by default return the same as the GET interface
+            result = shared.model_name
+
+            # Actions: info, load, list, unload
+            action = body.get('action', '')
+
+            if action == 'load':
+                model_name = body['model_name']
+                args = body.get('args', {})
+                print('args', args)
+                for k in args:
+                    setattr(shared.args, k, args[k])
+
+                shared.model_name = model_name
+                unload_model()
+
+                model_settings = get_model_settings_from_yamls(shared.model_name)
+                shared.settings.update(model_settings)
+                update_model_parameters(model_settings, initial=True)
+
+                if shared.settings['mode'] != 'instruct':
+                    shared.settings['instruction_template'] = None
+
+                try:
+                    shared.model, shared.tokenizer = load_model(shared.model_name)
+                    if shared.args.lora:
+                        add_lora_to_model(shared.args.lora)  # list
+
+                except Exception as e:
+                    response = json.dumps({'error': {'message': repr(e)}})
+
+                    self.wfile.write(response.encode('utf-8'))
+                    raise e
+
+                shared.args.model = shared.model_name
+
+                result = get_model_info()
+
+            elif action == 'unload':
+                unload_model()
+                shared.model_name = None
+                shared.args.model = None
+                result = get_model_info()
+
+            elif action == 'list':
+                result = get_available_models()
+
+            elif action == 'info':
+                result = get_model_info()
+
+            response = json.dumps({
+                'result': result,
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+
+        elif self.path == '/api/v1/token-count':
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            tokens = encode(body['prompt'])[0]
+            response = json.dumps({
+                'results': [{
+                    'tokens': len(tokens)
+                }]
+            })
+
+            self.wfile.write(response.encode('utf-8'))
+        else:
+            self.send_error(404)
+
+    def do_OPTIONS(self):
+        self.send_response(200)
+        self.end_headers()
+
+    def end_headers(self):
+        self.send_header('Access-Control-Allow-Origin', '*')
+        self.send_header('Access-Control-Allow-Methods', '*')
+        self.send_header('Access-Control-Allow-Headers', '*')
+        self.send_header('Cache-Control', 'no-store, no-cache, must-revalidate')
+        super().end_headers()
+
+
+def _run_server(port: int, share: bool = False, tunnel_id=str):
+    address = '0.0.0.0' if shared.args.listen else '127.0.0.1'
+
+    server = ThreadingHTTPServer((address, port), Handler)
+
+    def on_start(public_url: str):
+        print(f'Starting non-streaming server at public url {public_url}/api')
+
+    if share:
+        try:
+            try_start_cloudflared(port, tunnel_id, max_attempts=3, on_start=on_start)
+        except Exception:
+            pass
+    else:
+        print(
+            f'Starting API at http://{address}:{port}/api')
+
+    server.serve_forever()
+
+
+def start_server(port: int, share: bool = False, tunnel_id=str):
+    Thread(target=_run_server, args=[port, share, tunnel_id], daemon=True).start()

extensions/api/requirements.txt

@@ -0,0 +1,2 @@
+flask_cloudflared==0.0.14
+websockets==11.0.2

extensions/api/script.py

@@ -0,0 +1,13 @@
+import time
+
+import extensions.api.blocking_api as blocking_api
+import extensions.api.streaming_api as streaming_api
+from modules import shared
+
+
+def setup():
+    blocking_api.start_server(shared.args.api_blocking_port, share=shared.args.public_api, tunnel_id=shared.args.public_api_id)
+    if shared.args.public_api:
+        time.sleep(5)
+
+    streaming_api.start_server(shared.args.api_streaming_port, share=shared.args.public_api, tunnel_id=shared.args.public_api_id)

extensions/api/streaming_api.py

@@ -0,0 +1,124 @@
+import asyncio
+import json
+from threading import Thread
+
+from extensions.api.util import (
+    build_parameters,
+    try_start_cloudflared,
+    with_api_lock
+)
+from modules import shared
+from modules.chat import generate_chat_reply
+from modules.text_generation import generate_reply
+from websockets.server import serve
+
+PATH = '/api/v1/stream'
+
+
+@with_api_lock
+async def _handle_stream_message(websocket, message):
+    message = json.loads(message)
+
+    prompt = message['prompt']
+    generate_params = build_parameters(message)
+    stopping_strings = generate_params.pop('stopping_strings')
+    generate_params['stream'] = True
+
+    generator = generate_reply(
+        prompt, generate_params, stopping_strings=stopping_strings, is_chat=False)
+
+    # As we stream, only send the new bytes.
+    skip_index = 0
+    message_num = 0
+
+    for a in generator:
+        to_send = a[skip_index:]
+        if to_send is None or chr(0xfffd) in to_send:  # partial unicode character, don't send it yet.
+            continue
+
+        await websocket.send(json.dumps({
+            'event': 'text_stream',
+            'message_num': message_num,
+            'text': to_send
+        }))
+
+        await asyncio.sleep(0)
+        skip_index += len(to_send)
+        message_num += 1
+
+    await websocket.send(json.dumps({
+        'event': 'stream_end',
+        'message_num': message_num
+    }))
+
+
+@with_api_lock
+async def _handle_chat_stream_message(websocket, message):
+    body = json.loads(message)
+
+    user_input = body['user_input']
+    generate_params = build_parameters(body, chat=True)
+    generate_params['stream'] = True
+    regenerate = body.get('regenerate', False)
+    _continue = body.get('_continue', False)
+
+    generator = generate_chat_reply(
+        user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
+
+    message_num = 0
+    for a in generator:
+        await websocket.send(json.dumps({
+            'event': 'text_stream',
+            'message_num': message_num,
+            'history': a
+        }))
+
+        await asyncio.sleep(0)
+        message_num += 1
+
+    await websocket.send(json.dumps({
+        'event': 'stream_end',
+        'message_num': message_num
+    }))
+
+
+async def _handle_connection(websocket, path):
+
+    if path == '/api/v1/stream':
+        async for message in websocket:
+            await _handle_stream_message(websocket, message)
+
+    elif path == '/api/v1/chat-stream':
+        async for message in websocket:
+            await _handle_chat_stream_message(websocket, message)
+
+    else:
+        print(f'Streaming api: unknown path: {path}')
+        return
+
+
+async def _run(host: str, port: int):
+    async with serve(_handle_connection, host, port, ping_interval=None):
+        await asyncio.Future()  # run forever
+
+
+def _run_server(port: int, share: bool = False, tunnel_id=str):
+    address = '0.0.0.0' if shared.args.listen else '127.0.0.1'
+
+    def on_start(public_url: str):
+        public_url = public_url.replace('https://', 'wss://')
+        print(f'Starting streaming server at public url {public_url}{PATH}')
+
+    if share:
+        try:
+            try_start_cloudflared(port, tunnel_id, max_attempts=3, on_start=on_start)
+        except Exception as e:
+            print(e)
+    else:
+        print(f'Starting streaming server at ws://{address}:{port}{PATH}')
+
+    asyncio.run(_run(host=address, port=port))
+
+
+def start_server(port: int, share: bool = False, tunnel_id=str):
+    Thread(target=_run_server, args=[port, share, tunnel_id], daemon=True).start()

extensions/api/util.py

@@ -0,0 +1,148 @@
+import asyncio
+import functools
+import threading
+import time
+import traceback
+from threading import Thread
+from typing import Callable, Optional
+
+from modules import shared
+from modules.chat import load_character_memoized
+from modules.presets import load_preset_memoized
+
+# We use a thread local to store the asyncio lock, so that each thread
+# has its own lock.  This isn't strictly necessary, but it makes it
+# such that if we can support multiple worker threads in the future,
+# thus handling multiple requests in parallel.
+api_tls = threading.local()
+
+
+def build_parameters(body, chat=False):
+
+    generate_params = {
+        'max_new_tokens': int(body.get('max_new_tokens', body.get('max_length', 200))),
+        'auto_max_new_tokens': bool(body.get('auto_max_new_tokens', False)),
+        'max_tokens_second': int(body.get('max_tokens_second', 0)),
+        'do_sample': bool(body.get('do_sample', True)),
+        'temperature': float(body.get('temperature', 0.5)),
+        'top_p': float(body.get('top_p', 1)),
+        'typical_p': float(body.get('typical_p', body.get('typical', 1))),
+        'epsilon_cutoff': float(body.get('epsilon_cutoff', 0)),
+        'eta_cutoff': float(body.get('eta_cutoff', 0)),
+        'tfs': float(body.get('tfs', 1)),
+        'top_a': float(body.get('top_a', 0)),
+        'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))),
+        'repetition_penalty_range': int(body.get('repetition_penalty_range', 0)),
+        'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)),
+        'top_k': int(body.get('top_k', 0)),
+        'min_length': int(body.get('min_length', 0)),
+        'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size', 0)),
+        'num_beams': int(body.get('num_beams', 1)),
+        'penalty_alpha': float(body.get('penalty_alpha', 0)),
+        'length_penalty': float(body.get('length_penalty', 1)),
+        'early_stopping': bool(body.get('early_stopping', False)),
+        'mirostat_mode': int(body.get('mirostat_mode', 0)),
+        'mirostat_tau': float(body.get('mirostat_tau', 5)),
+        'mirostat_eta': float(body.get('mirostat_eta', 0.1)),
+        'guidance_scale': float(body.get('guidance_scale', 1)),
+        'negative_prompt': str(body.get('negative_prompt', '')),
+        'seed': int(body.get('seed', -1)),
+        'add_bos_token': bool(body.get('add_bos_token', True)),
+        'truncation_length': int(body.get('truncation_length', body.get('max_context_length', 2048))),
+        'ban_eos_token': bool(body.get('ban_eos_token', False)),
+        'skip_special_tokens': bool(body.get('skip_special_tokens', True)),
+        'custom_stopping_strings': '',  # leave this blank
+        'stopping_strings': body.get('stopping_strings', []),
+    }
+
+    preset_name = body.get('preset', 'None')
+    if preset_name not in ['None', None, '']:
+        preset = load_preset_memoized(preset_name)
+        generate_params.update(preset)
+
+    if chat:
+        character = body.get('character')
+        instruction_template = body.get('instruction_template', shared.settings['instruction_template'])
+        if str(instruction_template) == "None":
+            instruction_template = "Vicuna-v1.1"
+
+        name1, name2, _, greeting, context, _ = load_character_memoized(character, str(body.get('your_name', shared.settings['name1'])), shared.settings['name2'], instruct=False)
+        name1_instruct, name2_instruct, _, _, context_instruct, turn_template = load_character_memoized(instruction_template, '', '', instruct=True)
+        generate_params.update({
+            'mode': str(body.get('mode', 'chat')),
+            'name1': str(body.get('name1', name1)),
+            'name2': str(body.get('name2', name2)),
+            'context': str(body.get('context', context)),
+            'greeting': str(body.get('greeting', greeting)),
+            'name1_instruct': str(body.get('name1_instruct', name1_instruct)),
+            'name2_instruct': str(body.get('name2_instruct', name2_instruct)),
+            'context_instruct': str(body.get('context_instruct', context_instruct)),
+            'turn_template': str(body.get('turn_template', turn_template)),
+            'chat-instruct_command': str(body.get('chat_instruct_command', body.get('chat-instruct_command', shared.settings['chat-instruct_command']))),
+            'history': body.get('history', {'internal': [], 'visible': []})
+        })
+
+    return generate_params
+
+
+def try_start_cloudflared(port: int, tunnel_id: str, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
+    Thread(target=_start_cloudflared, args=[
+           port, tunnel_id, max_attempts, on_start], daemon=True).start()
+
+
+def _start_cloudflared(port: int, tunnel_id: str, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
+    try:
+        from flask_cloudflared import _run_cloudflared
+    except ImportError:
+        print('You should install flask_cloudflared manually')
+        raise Exception(
+            'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.')
+
+    for _ in range(max_attempts):
+        try:
+            if tunnel_id is not None:
+                public_url = _run_cloudflared(port, port + 1, tunnel_id=tunnel_id)
+            else:
+                public_url = _run_cloudflared(port, port + 1)
+
+            if on_start:
+                on_start(public_url)
+
+            return
+        except Exception:
+            traceback.print_exc()
+            time.sleep(3)
+
+        raise Exception('Could not start cloudflared.')
+
+
+def _get_api_lock(tls) -> asyncio.Lock:
+    """
+    The streaming and blocking API implementations each run on their own
+    thread, and multiplex requests using asyncio.  If multiple outstanding
+    requests are received at once, we will try to acquire the shared lock
+    shared.generation_lock multiple times in succession in the same thread,
+    which will cause a deadlock.
+
+    To avoid this, we use this wrapper function to block on an asyncio
+    lock, and then try and grab the shared lock only while holding
+    the asyncio lock.
+    """
+    if not hasattr(tls, "asyncio_lock"):
+        tls.asyncio_lock = asyncio.Lock()
+
+    return tls.asyncio_lock
+
+
+def with_api_lock(func):
+    """
+    This decorator should be added to all streaming API methods which
+    require access to the shared.generation_lock.  It ensures that the
+    tls.asyncio_lock is acquired before the method is called, and
+    released afterwards.
+    """
+    @functools.wraps(func)
+    async def api_wrapper(*args, **kwargs):
+        async with _get_api_lock(api_tls):
+            return await func(*args, **kwargs)
+    return api_wrapper

extensions/character_bias/script.py

@@ -0,0 +1,83 @@
+import os
+
+import gradio as gr
+
+# get the current directory of the script
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# check if the bias_options.txt file exists, if not, create it
+bias_file = os.path.join(current_dir, "bias_options.txt")
+if not os.path.isfile(bias_file):
+    with open(bias_file, "w") as f:
+        f.write("*I am so happy*\n*I am so sad*\n*I am so excited*\n*I am so bored*\n*I am so angry*")
+
+# read bias options from the text file
+with open(bias_file, "r") as f:
+    bias_options = [line.strip() for line in f.readlines()]
+
+params = {
+    "activate": True,
+    "bias string": " *I am so happy*",
+    "use custom string": False,
+}
+
+
+def input_modifier(string):
+    """
+    This function is applied to your text inputs before
+    they are fed into the model.
+    """
+    return string
+
+
+def output_modifier(string):
+    """
+    This function is applied to the model outputs.
+    """
+    return string
+
+
+def bot_prefix_modifier(string):
+    """
+    This function is only applied in chat mode. It modifies
+    the prefix text for the Bot and can be used to bias its
+    behavior.
+    """
+    if params['activate']:
+        if params['use custom string']:
+            return f'{string} {params["custom string"].strip()} '
+        else:
+            return f'{string} {params["bias string"].strip()} '
+    else:
+        return string
+
+
+def ui():
+    # Gradio elements
+    activate = gr.Checkbox(value=params['activate'], label='Activate character bias')
+    dropdown_string = gr.Dropdown(choices=bias_options, value=params["bias string"], label='Character bias', info='To edit the options in this dropdown edit the "bias_options.txt" file')
+    use_custom_string = gr.Checkbox(value=False, label='Use custom bias textbox instead of dropdown')
+    custom_string = gr.Textbox(value="", placeholder="Enter custom bias string", label="Custom Character Bias", info='To use this textbox activate the checkbox above')
+
+    # Event functions to update the parameters in the backend
+    def update_bias_string(x):
+        if x:
+            params.update({"bias string": x})
+        else:
+            params.update({"bias string": dropdown_string.get()})
+        return x
+
+    def update_custom_string(x):
+        params.update({"custom string": x})
+
+    dropdown_string.change(update_bias_string, dropdown_string, None)
+    custom_string.change(update_custom_string, custom_string, None)
+    activate.change(lambda x: params.update({"activate": x}), activate, None)
+    use_custom_string.change(lambda x: params.update({"use custom string": x}), use_custom_string, None)
+
+    # Group elements together depending on the selected option
+    def bias_string_group():
+        if use_custom_string.value:
+            return gr.Group([use_custom_string, custom_string])
+        else:
+            return dropdown_string